The significance of the patent translation pattern was shown in the translation quality of sentences translated from English patent documents into Korean patent documents . When sentences were translated by the machine translation system including patent translation patterns , the whole translation accuracy from 0 to 4 as evaluation grade was 82.43 % and the translation rate over 3 grade 87.60 % . But when the patent translation patterns were not included , the whole translation rate was 81.23 % and the translation rate over 3 grade 76.00 % . The project is conducted by the close cooperation between four organizations . At Kyoto University , we have the responsibility of developing the software system for the core part of the machine translation process ( grammar writing system and execution system ) ; grammar systems for analysis , transfer and synthesis ; detailed specification of what information is written in the word dictionaries ( all the parts of speech in the analysis , transfer , and generation dictionaries ) , and the working manuals for constructing these dictionaries . The Electrotechnical Laboratories ( ETL ) are responsible for the machine translation text input and output , morphological analysis and synthesis , and the construction of the verb and adjective dictionaries based on the working manuals prepared at Kyoto . In commercial settings , issues such as processing speed as well as the ability to adapt models to the customers ’ needs play a significant role and have a big impact on the choice of approaches that are added to the custom pipeline to deal with specific problems such as long-range reorderings . We developed a fast and customisable preordering component , also available as an open-source tool , which comes along with a generic implementation that is restricted neither to the translation platform nor to the Machine Translation paradigm . We test preordering on three language pairs : English →Japanese/German/Chinese for both Statistical Machine Translation ( SMT ) and Neural Machine Translation ( NMT ) . With the development of Internet , the people 's demand for the machine-based translation system or machine-aided translation system is more and more urgent , and the research on machine-based translation becomes the hotspot . This paper expounds the design principles of machine-based translation system , advances the design frame of machine-based translation system , and implements successfully the designed functions . Developing an automatic system to identify speakers as native or non-native , as well as their native language , including dialect , within input audio streams , is the major goal of this project . Such a system may be used alone or with other downstream applications such as machine language translation systems . In this paper we present four approaches to identify native and non-native speakers as a binary recognition problem . From this experiment , we have found the following . ( 1 ) It is important that a machine translation machine make it visible in a 3D virtual space . A user can easily learn the usage of a machine translation . A translator 's sword of Damocles ? An introduction to machine translation Popular conceptions about machine translation Machine translation and the roller coaster of history Machine translation - definitions , architectures and quality demands MT architectures Strategies for optimizing the quality of MT output The practical use of MT technology - high-end versus low-end systems Notorious problems in MT MT on the Internet Tasks Further reading and Internet links Glossary References Index There is need to fill the gap between two entities which use different languages to communicate which can be done either via human translation or machine translation means . The world has over 7000 living natural languages , thus making it costly if human translation route is taken hence need for machine translation . In view of practical machine translation using , this paper provides a design proposal of Translation Robot reconstruction on the basis of knowledge management technique . Through the integration of knowledge management technology and machine translation technology , the obtaining capability and the applying capability of a translation system on knowledge are further improved , and consequently , the intelligence and practicality of a machine translation system are improved . In this paper , we discuss different aspects of pronominal divergence that affects the anaphora resolution in English Hindi Machine Translation ( EHMT ) . The study shall be helpful in developing approaches that can explicitly use inter-sentential information in order to resolve specific types of ambiguity and which can generate coherent multi-sentence discourse structure in the target language to produce higher quality of translation Machine Translation . We propose a new simple network architecture , the Transformer , based solely on attention mechanisms , dispensing with recurrence and convolutions entirely . Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train . Our model achieves 28.4 BLEU on the WMT 2014 English - to - German translation task , improving over the existing best results , including ensembles , by over 2 BLEU . Selecting existing sentences to form the sentence summary is impossible . Early studies on sentence summarization involve handcrafted rules , syntactic tree pruning and statistical machine translation techniques . Recently , the application of the attentional sequence - to - sequence ( s2s ) framework has attracted growing attention in this area . Its core idea is somewhat similar to the idea introduced in Section [ reference ] and Section [ reference ] of this work . Adapted - transformer : a transformer vaswani2017attention based GEC system junczys2018approaching with techniques adapted from low - resource machine translation . SMT - NMT hybrid : the state - of - the - art GEC system grundkiewicz2018near that is based on an SMT - NMT hybrid approach . Vision - to - Language problems present a particular challenge in Computer Vision because they require translation between two different forms of information . In this sense the problem is similar to that of machine translation between languages . In machine language translation there have been a series of results showing that good performance can be achieved without developing a higher - level model of the state of the world . In , for instance , a source sentence is transformed into a fixed - length vector representation by an ‘ encoder ’ RNN , which in turn is used as the initial hidden state of a ‘ decoder ’ RNN that generates the target sentence . Nevertheless , these methods were only shown to work in limited settings , being most often evaluated in word - level translation . More recently , the task got a renewed interest after the concurrent work of artetxe2018unmt and lample2018unsupervised on unsupervised NMT which , for the first time , obtained promising results in standard machine translation benchmarks using monolingual corpora only . Both methods build upon the recent work on unsupervised cross - lingual embedding mappings , which independently train word embeddings in two languages and learn a linear transformation to map them to a shared space through self - learning artetxe2017learning , artetxe2018robust or adversarial training conneau2018word . As opposed to GCNs , our model allows for ( implicitly ) assigning different importances to nodes of a same neighborhood , enabling a leap in model capacity . Furthermore , analyzing the learned attentional weights may lead to benefits in interpretability , as was the case in the machine translation domain ( e.g. the qualitative analysis of [ reference ] ) . • 4 - grams , and it is computed at the corpus level ( except in the image captioning case ) as this seems the most common practice in the summarization and machine translation literature . we have added several references as suggested by our reviewers we have shortened the paper by moving some content to the Supplementary Material . On the Google Production dataset , our model achieved 1.01 higher test BLEU score even after training for only one sixth of the time . section : MULTILINGUAL MACHINE TRANSLATION Dataset : ( Johnson et al . , 2016 ) train a single GNMT [ reference ] ) Verification methods based on SAT , SMT , and theorem proving often rely on proofs of unsatisfiability as a powerful tool to extract information in order to reduce the overall effort . For example a proof may be traversed to identify a minimal reason that led to unsatisfiability , for computing abstractions , or for deriving Craig interpolants . It is therefore a promising direction to combine the advantages of both NMT and SMT . In this paper , we propose a neural system combination framework leveraging multi-source NMT , which takes as input the outputs of NMT and SMT systems and produces the final translation . Extensive experiments on the Chinese-to-English translation task show that our model archives significant improvement by 5.3 BLEU points over the best single system output and 3.4 BLEU points over the state-of-the-art traditional system combination methods . Both results are much higher than predicted by past research , which claims coverage for only duplicated instructions , or 65 % of instructions . On an 8-issue SMT processor , Slipstream performs within 1.3 % of single-thread execution whereas full duplication slows performance by 14%.A key byproduct of this paper is a novel analysis framework in which every dynamic instruction is considered to be hypothetically faulty , thus not requiring explicit fault injection . Fault coverage is measured in terms of the fraction of candidate faulty instructions that are directly or indirectly detectable before . This paper describes an automated postediting submission to the DiscoMT 2015 shared task on pronoun translation . Postediting is achieved by applying pronounspecific rules to the output of an Englishto-French phrase-based SMT system . These results provide an effective way to reason about the robustness of a classifier ensemble . We provide SMT and MILP encodings to compute optimal randomized attacks or prove that there is no attack inducing a certain expected loss . In the latter case , the classifier ensemble is provably robust . This observation tends to be supported in the published literature . An article by Don Revelino of Packard Bell , titled Achieving Single Digit DPMO in SMT Processes , states that " world-class " is 50 ppm and Packard Bell achieved single-digit DPMO . A more recent document by Sitg Oresjo of Agilent ( The Why , Where , What , How and When of Automated X-ray Inspection ) also implies 50 ppm is a good defect rate . Phrase based statistic MT ( SMT ) is an important milestone in MT . However , the translation model in the phrase based SMT is structure free which limits its reordering capacity to some extent . This paper proposes Morph Core , a unique approach to satisfying these competing requirements , by starting with a traditional high performance out-of-order core and making minimal changes that can transform it into a highly-threaded in-order SMT core when necessary . The result is a micro architecture that outperforms an aggressive 4-way SMT out-of-order core , " medium " out-of-order cores , small in-order cores , and Core Fusion . Compared to a 2-way SMT out-of-order core , Morph Core increases performance by 10 % and reduces energy-delay-squared product by 22 % . The topic has got a renewed interest after the recent irruption of Neural Machine Translation ( NMT ) , which is known to be particularly sensitive to the size and quality of the training data . This way , even if NMT has been shown to be vastly superior to Statistical Machine Translation ( SMT ) in standard benchmarks wu2016google , SMT still beats NMT when the training corpus is relatively small koehn2017six , and it is also substantially more robust to noisy training data khayrallah2018impact , thus increasing the need for large and high - quality parallel corpora . In this paper , we investigate two supervised approaches for generating all types of artificial errors . We propose a framework for generating errors based on statistical machine translation ( SMT ) , training a model to translate from correct into incorrect sentences . In addition , we describe a method for learning error patterns from an annotated corpus and transplanting them into error - free text . In this paper we compare error detection frameworks trained on the same publicly available FCE dataset , thereby removing the confounding factor of dataset size and only focusing on the model architectures . The error generation methods can generate alternative versions of the same input text - the pattern - based method randomly samples the error locations , and the SMT system can provide an n - best list of alternative translations . Therefore , we also investigated the combination of multiple error - generated versions of the input files when training error detection models . Nevertheless , these early systems were later superseded by Statistical Machine Translation ( SMT ) based approaches , which induced an initial phrase - table through cross - lingual embedding mappings , combined it with an n - gram language model , and further improved the system through iterative back - translation lample2018phrase , artetxe2018usmt . In this paper , we develop a more principled approach to unsupervised SMT , addressing several deficiencies of previous systems by incorporating subword information , applying a theoretically well founded unsupervised tuning method , and developing a joint refinement procedure . In addition to that , we use our improved SMT approach to initialize an unsupervised NMT system , which is further improved through on - the - fly back - translation . Results are shown in Table 8 . In phrasebased SMT , we find that the use of back - translated training data has a moderate positive effect on the WMT test sets ( + 0.7 BLEU ) , but not on the IWSLT test sets . This is in line with the expectation that the main effect of back - translated data for phrase - based SMT is domain adaptation [ reference ] . This is by far the best result achieved by direct translation with large neural networks . For comparison , the BLEU score of an SMT baseline on this dataset is 33.30 . The 34.81 BLEU score was achieved by an LSTM with a vocabulary of 80k words , so the score was penalized whenever the reference translation contained a word not covered by these 80k . In the field of statistical machine translation ( SMT ) , deep neural networks have begun to show promising results . summarizes a successful usage of feedforward neural networks in the framework of phrase - based SMT system . Along this line of research on using neural networks for SMT , this paper focuses on a novel neural network architecture that can be used as a part of the conventional phrase - based SMT system . AMU14 and AMU16 : SMT - based GEC systems junczys2014amu , junczys2016phrase developed by AMU . NUS14 , NUS16 , NUS17 and section : Related Work LSTM [ reference ] showed its early potentials in NLP when a neural machine translation system that leverages LSTM source encoding gave highly competitive results compared to the best SMT models [ reference ] . LSTM encoders have since been explored for other tasks , including syntactic parsing [ reference ] , text classification [ reference ] and machine reading In this work , we use the Statistical Machine Translation approach . Statistical Machine Translation systems perform poorly when applied on new domains . The domain adaptation problem has recently gained interest in Statistical Machine Translation . Our analysis shows the superiority of convolutional neural networks over recurrent neural networks such as long short-term memory ( LSTM ) networks in capturing the local context via attention , and thereby improving the coverage in correcting grammatical errors . By ensembling multiple models , and incorporating an N-gram language model and edit features via rescoring , our novel method becomes the first neural approach to outperform the current state-of-the-art statistical machine translation-based approach , both in terms of grammaticality and fluency . At recent time , the statistical based language model and neural based language model are still dominating the researches in the field of machine translation . The statistical based machine translation today is the fastest one but it has a weakness in term of accuracy . In contrast , the neural based network has higher accuracy but has a very slow computation process . Objective The objective of this work is to use scenario-based methods with EMS providers and nonnative English-speaking users who identified themselves as LEP ( henceforth referred to as LEP participants ) to evaluate the potential of two automated translation technologies in improving emergency communication . Methods We developed mock emergency scenarios and enacted them in simulation sessions with EMS personnel and Spanish-speaking and Chinese-speaking ( Mandarin ) LEP participants using two automated language translation tools : an EMS domain-specific fixed-sentence translation tool ( QuickSpeak ) and a statistical machine translation tool ( Google Translate ) . At the end of the sessions , we gathered feedback from both groups through a postsession questionnaire . We describe the design of an autonomous agent that can teach itself how to translate from a foreign language , by first assembling its own training set , then using it to improve its vocabulary and language model . The key idea is that a Statistical Machine Translation package can be used for the Cross-Language Retrieval Task of assembling a training set from a vast amount of available text e.g. a large multilingual corpus , or the Web and then train on that data , repeating the process several times . The stability issues related to such a feedback loop are addressed by a mathematical model , connecting statistical and control-theoretic aspects of the system . In order to cope with the issues , this work proposes a new model to correct spacing in multi-part words . The proposed method is based on statistical machine translation paradigm . In machine translation paradigm , text in source language is translated into a text in destination language on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora . We adopt a unified approach to the problem . We frame the problem from a statistical Machine Translation perspective . We further post edit the output applying linguistically informed rules particular to the language pair and re-rank the output using machine learning methods . A method based on statistical machine translation is presented that will enable robots to learn language . The proposed method is novel in two ways : ( 1 ) the language acquisition problem is formulated as a statistical machine translation problem , and ( 2 ) language-specific morphological analysis is not necessary . Promising experimental results were obtained by applying a traditional statistical machine translation method for sequence-to-sequence conversion . We introduce a greedy algorithm called Local-(k , l)-Step , and show that it performs better than the DP-based algorithm . Our word-reordering algorithm can be used in the statistical machine translation process for improving the quality of the translation . Furthermore , motivated by the rank evaluation method , we introduce a novel way for evaluating the results of word-reordering by calculating the inversion pair cardinality . An example of one-to-many mapping is Englishâ � � Indonesian preposition . This preposition translation mapping can be worked out using statistical method machine translation . The phrase-based method that embedded with the statistical method makes the machine translation become more powerful in handling preposition translation . Recently , Sutskever et al . introduced a neural network model for solving the general sequence - to - sequence problem , and Bahdanau et al . proposed a related model with an attention mechanism that makes it capable of handling long sequences well . Both models achieve state - of - the - art results on large scale machine translation tasks ( e.g. , ) . Syntactic constituency parsing can be formulated as a sequence - to - sequence problem if we linearize the parse tree ( cf . Neural Machine Translation ( NMT ) has obtained state - of - the art performance for several language pairs , while only using parallel data for training . Targetside monolingual data plays an important role in boosting fluency for phrasebased statistical machine translation , and we investigate the use of monolingual data for NMT . In contrast to previous work , which combines NMT models with separately trained language models , we note that encoder - decoder NMT architectures already have the capacity to learn the same information as a language model , and we explore strategies to train with monolingual data without changing the neural network architecture . First , we summarize the research trends of papers presented in the proceedings . Second , we describe the results of the two shared tasks 1 ) efficient neural machine translation ( NMT ) where participants were tasked with creating NMT systems that are both accurate and efficient , and 2 ) document-level generation and translation ( DGT ) where participants were tasked with developing systems that generate summaries from structured data , potentially with assistance from text in another language . In recent years , Neural Machine Translation ( NMT ) has been proven to get impressive results . While some additional linguistic features of input words improve wordlevel NMT , any additional character features have not been used to improve character-level NMT so far . In this paper , we show that the radicals of Chinese characters ( or kanji ) , as a character feature information , can be easily provide further improvements in the character-level NMT . Also , pedestrians׳ preference of attributes in relation to different road classes highly agrees with current design principles . It was found that gender , age of pedestrian , purpose of trip and road class affect pedestrian preference and if considered in the planning and design of crossing facilities , they can result in improved safety , convenience and use of this Non-Motorised Transport ( NMT ) mode . In this paper , we introduce a hybrid search for attention-based neural machine translation ( NMT ) . A target phrase learned with statistical MT models extends a hypothesis in the NMT beam search when the attention of the NMT model focuses on the source words translated by this phrase . Phrases added in this way are scored with the NMT model , but also with SMT features including phrase-level translation probabilities and a target language model . It thus does not explicitly balance the contribution from word embeddings and the contribution from additional linguistic knowledge . To address these limitations , this paper proposes a knowledge-aware NMT approach that models additional linguistic features in parallel to the word feature . The core idea is that we propose modeling a series of linguistic features at the word level ( knowledge block ) using a recurrent neural network ( RNN ) . Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language . Using a shared wordpiece vocabulary , our approach enables Multilingual NMT systems using a single model . On the WMT’14 benchmarks , a single multilingual model achieves comparable performance for English→French and surpasses state-of-theart results for English→German . An approach to boundary evaluation and a boundary representation are presented . The approach uses nonmanifold topology ( NMT ) to overcome editing and coverage limitations associated with the manifold nature of existing boundary evaluation schemes . It merges the input primitives into a single representation and selects the entities in the merged set that constitute the result of the Boolean operations . paragraph : NLI as a supervised training set Our findings indicate that our model trained on SNLI obtains much better overall results than models trained on other supervised tasks such as COCO , dictionary definitions , NMT , PPDB and SST . For SST , we tried exactly the same models as for SNLI ; it is worth noting that SST is smaller than NLI . section : Abstract Neural Machine Translation ( NMT ) has obtained state - of - the art performance for several language pairs , while only using parallel data for training . Targetside monolingual data plays an important role in boosting fluency for phrasebased statistical machine translation , and we investigate the use of monolingual data for NMT . In contrast to previous work , which combines NMT models with separately trained language models , we note that encoder - decoder NMT architectures already have the capacity to learn the same information as a language model , and we explore strategies to train with monolingual data without changing the neural network architecture . By pairing monolingual training data with an automatic backtranslation , we can treat it as additional parallel training data , and we obtain substantial improvements on the WMT 15 task English↔German To obtain a synthetic parallel training set , we back - translate a random sample of 3 200 000 sentences from Gigaword . We use an English→Turkish NMT system trained with the same settings as the Turkish→English baseline system . We found overfitting to be a bigger problem than with the larger English↔German data set , and follow [ reference ] in using Gaussian noise ( stddev 0.01 ) [ reference ] , and dropout on the output layer ( p=0.5 ) ment of 2.9 BLEU . While it is unsurprising that in - domain parallel data is most valuable , we find it encouraging that NMT domain adaptation with monolingual data is also possible , and effective , since there are settings where only monolingual in - domain data is available . The best results published on this dataset are by , obtained with an ensemble of 8 independently trained models . In addition to that , we show that our method can also bring large improvements to downstream machine translation . An NMT model trained on our filtered version of ParaCrawl obtains 31.2 BLEU points on the standard English - German newstest2014 benchmark , an improvement of more than one point over an equivalent system trained on the official filtered version . section : Related work Several architectures have been proposed to reduce the computational load associated with recurrence - based computation gehring2016convenc , gehring2017convs2s , kaiser2016can , kalchbrenner2016neural . Self - attention , which relies on dot - products between elements of the input sequence to compute a weighted sum lin2017structured , bahdanau2014neural , parikh2016decomposable , kim2017structured , has also been a critical ingredient in modern NMT architectures . The Transformer network vaswani2017attention avoids the recurrence completely and uses only self - attention . Adapted - transformer : a transformer vaswani2017attention based GEC system junczys2018approaching with techniques adapted from low - resource machine translation . SMT - NMT hybrid : the state - of - the - art GEC system grundkiewicz2018near that is based on an SMT - NMT hybrid approach . Table [ reference ] shows the results of GEC systems on CoNLL and JFLEG dataset . We apply dropout to the output of each LSTM layer in the decoder . Our implementation is heavily based off of the Google NMT Tutorial luong17 . We attribute our significantly better results to using pre - trained word embeddings , a character - level CNN , a larger model , stronger regularization , and better hyperparameter tuning . Compared with a heuristic similarity based method , the proposed method significantly improves the performance with only limited sub-tree aligned data . To examine its effectiveness for multilingual applications , we further attempt different approaches to apply the sub-tree alignment in both phrase and syntax based SMT systems . We then compare the performance with that of the widely used word alignment . section : Contrast to Phrase - based SMT The back - translation of monolingual target data into the source language to produce synthetic parallel text has been previously explored for phrase - based SMT [ reference ][ reference ] . Our best results are obtained with an ensemble of LSTMs that differ in their random initializations and in the random order of minibatches . While the decoded translations of the LSTM ensemble do not outperform the best WMT’14 system , it is the first time that a pure neural translation system outperforms a phrase - based SMT baseline on a large scale MT task by a sizeable margin , despite its inability to handle out - of - vocabulary words . The LSTM is within 0.5 BLEU points of the best WMT’14 result if it is used to rescore the 1000 - best list of the baseline system . The reverse of this idea , translating from incorrect to correct sentences , has been shown to work well for error correction tasks [ reference ][ reference ] , and roundtrip translation has also been shown to be promising for correcting grammatical errors [ reference ] . Following previous work [ reference ][ reference ] , we build a phrase - based SMT error generation system . During training , error - corrected sentences in the training data are treated as the source , and the original sentences written by language learners as the target . Section [ reference ] first discusses the related work in the topic . Section [ reference ] then describes our principled unsupervised SMT method , while Section [ reference ] discusses our hybridization method with NMT . We then present the experiments done and the results obtained in Section [ reference ] , and Section [ reference ] concludes the paper .
The significance of the patent translation pattern was shown in the translation quality of sentences translated from English patent documents into Korean patent documents . When sentences were translated by the machine translation system including patent translation patterns , the whole translation accuracy from 0 to 4 as evaluation grade was 82.43 % and the translation rate over 3 grade 87.60 % . But when the patent translation patterns were not included , the whole translation rate was 81.23 % and the translation rate over 3 grade 76.00 % .
The project is conducted by the close cooperation between four organizations . At Kyoto University , we have the responsibility of developing the software system for the core part of the machine translation process ( grammar writing system and execution system ) ; grammar systems for analysis , transfer and synthesis ; detailed specification of what information is written in the word dictionaries ( all the parts of speech in the analysis , transfer , and generation dictionaries ) , and the working manuals for constructing these dictionaries . The Electrotechnical Laboratories ( ETL ) are responsible for the machine translation text input and output , morphological analysis and synthesis , and the construction of the verb and adjective dictionaries based on the working manuals prepared at Kyoto .
In commercial settings , issues such as processing speed as well as the ability to adapt models to the customers ’ needs play a significant role and have a big impact on the choice of approaches that are added to the custom pipeline to deal with specific problems such as long-range reorderings . We developed a fast and customisable preordering component , also available as an open-source tool , which comes along with a generic implementation that is restricted neither to the translation platform nor to the Machine Translation paradigm . We test preordering on three language pairs : English →Japanese/German/Chinese for both Statistical Machine Translation ( SMT ) and Neural Machine Translation ( NMT ) .
With the development of Internet , the people 's demand for the machine-based translation system or machine-aided translation system is more and more urgent , and the research on machine-based translation becomes the hotspot . This paper expounds the design principles of machine-based translation system , advances the design frame of machine-based translation system , and implements successfully the designed functions .
Developing an automatic system to identify speakers as native or non-native , as well as their native language , including dialect , within input audio streams , is the major goal of this project . Such a system may be used alone or with other downstream applications such as machine language translation systems . In this paper we present four approaches to identify native and non-native speakers as a binary recognition problem .
From this experiment , we have found the following . ( 1 ) It is important that a machine translation machine make it visible in a 3D virtual space . A user can easily learn the usage of a machine translation .
A translator 's sword of Damocles ? An introduction to machine translation Popular conceptions about machine translation Machine translation and the roller coaster of history Machine translation - definitions , architectures and quality demands MT architectures Strategies for optimizing the quality of MT output The practical use of MT technology - high-end versus low-end systems Notorious problems in MT MT on the Internet Tasks Further reading and Internet links Glossary References Index
There is need to fill the gap between two entities which use different languages to communicate which can be done either via human translation or machine translation means . The world has over 7000 living natural languages , thus making it costly if human translation route is taken hence need for machine translation .
In view of practical machine translation using , this paper provides a design proposal of Translation Robot reconstruction on the basis of knowledge management technique . Through the integration of knowledge management technology and machine translation technology , the obtaining capability and the applying capability of a translation system on knowledge are further improved , and consequently , the intelligence and practicality of a machine translation system are improved .
In this paper , we discuss different aspects of pronominal divergence that affects the anaphora resolution in English Hindi Machine Translation ( EHMT ) . The study shall be helpful in developing approaches that can explicitly use inter-sentential information in order to resolve specific types of ambiguity and which can generate coherent multi-sentence discourse structure in the target language to produce higher quality of translation Machine Translation .
We propose a new simple network architecture , the Transformer , based solely on attention mechanisms , dispensing with recurrence and convolutions entirely . Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train . Our model achieves 28.4 BLEU on the WMT 2014 English - to - German translation task , improving over the existing best results , including ensembles , by over 2 BLEU .
Selecting existing sentences to form the sentence summary is impossible . Early studies on sentence summarization involve handcrafted rules , syntactic tree pruning and statistical machine translation techniques . Recently , the application of the attentional sequence - to - sequence ( s2s ) framework has attracted growing attention in this area .
Its core idea is somewhat similar to the idea introduced in Section [ reference ] and Section [ reference ] of this work . Adapted - transformer : a transformer vaswani2017attention based GEC system junczys2018approaching with techniques adapted from low - resource machine translation . SMT - NMT hybrid : the state - of - the - art GEC system grundkiewicz2018near that is based on an SMT - NMT hybrid approach .
Vision - to - Language problems present a particular challenge in Computer Vision because they require translation between two different forms of information . In this sense the problem is similar to that of machine translation between languages . In machine language translation there have been a series of results showing that good performance can be achieved without developing a higher - level model of the state of the world . In , for instance , a source sentence is transformed into a fixed - length vector representation by an ‘ encoder ’ RNN , which in turn is used as the initial hidden state of a ‘ decoder ’ RNN that generates the target sentence .
Nevertheless , these methods were only shown to work in limited settings , being most often evaluated in word - level translation . More recently , the task got a renewed interest after the concurrent work of artetxe2018unmt and lample2018unsupervised on unsupervised NMT which , for the first time , obtained promising results in standard machine translation benchmarks using monolingual corpora only . Both methods build upon the recent work on unsupervised cross - lingual embedding mappings , which independently train word embeddings in two languages and learn a linear transformation to map them to a shared space through self - learning artetxe2017learning , artetxe2018robust or adversarial training conneau2018word .
As opposed to GCNs , our model allows for ( implicitly ) assigning different importances to nodes of a same neighborhood , enabling a leap in model capacity . Furthermore , analyzing the learned attentional weights may lead to benefits in interpretability , as was the case in the machine translation domain ( e.g. the qualitative analysis of [ reference ] ) . •
4 - grams , and it is computed at the corpus level ( except in the image captioning case ) as this seems the most common practice in the summarization and machine translation literature . we have added several references as suggested by our reviewers we have shortened the paper by moving some content to the Supplementary Material .
On the Google Production dataset , our model achieved 1.01 higher test BLEU score even after training for only one sixth of the time . section : MULTILINGUAL MACHINE TRANSLATION Dataset : ( Johnson et al . , 2016 ) train a single GNMT [ reference ] )
Verification methods based on SAT , SMT , and theorem proving often rely on proofs of unsatisfiability as a powerful tool to extract information in order to reduce the overall effort . For example a proof may be traversed to identify a minimal reason that led to unsatisfiability , for computing abstractions , or for deriving Craig interpolants .
It is therefore a promising direction to combine the advantages of both NMT and SMT . In this paper , we propose a neural system combination framework leveraging multi-source NMT , which takes as input the outputs of NMT and SMT systems and produces the final translation . Extensive experiments on the Chinese-to-English translation task show that our model archives significant improvement by 5.3 BLEU points over the best single system output and 3.4 BLEU points over the state-of-the-art traditional system combination methods .
Both results are much higher than predicted by past research , which claims coverage for only duplicated instructions , or 65 % of instructions . On an 8-issue SMT processor , Slipstream performs within 1.3 % of single-thread execution whereas full duplication slows performance by 14%.A key byproduct of this paper is a novel analysis framework in which every dynamic instruction is considered to be hypothetically faulty , thus not requiring explicit fault injection . Fault coverage is measured in terms of the fraction of candidate faulty instructions that are directly or indirectly detectable before .
This paper describes an automated postediting submission to the DiscoMT 2015 shared task on pronoun translation . Postediting is achieved by applying pronounspecific rules to the output of an Englishto-French phrase-based SMT system .
These results provide an effective way to reason about the robustness of a classifier ensemble . We provide SMT and MILP encodings to compute optimal randomized attacks or prove that there is no attack inducing a certain expected loss . In the latter case , the classifier ensemble is provably robust .
This observation tends to be supported in the published literature . An article by Don Revelino of Packard Bell , titled Achieving Single Digit DPMO in SMT Processes , states that " world-class " is 50 ppm and Packard Bell achieved single-digit DPMO . A more recent document by Sitg Oresjo of Agilent ( The Why , Where , What , How and When of Automated X-ray Inspection ) also implies 50 ppm is a good defect rate .
Phrase based statistic MT ( SMT ) is an important milestone in MT . However , the translation model in the phrase based SMT is structure free which limits its reordering capacity to some extent .
This paper proposes Morph Core , a unique approach to satisfying these competing requirements , by starting with a traditional high performance out-of-order core and making minimal changes that can transform it into a highly-threaded in-order SMT core when necessary . The result is a micro architecture that outperforms an aggressive 4-way SMT out-of-order core , " medium " out-of-order cores , small in-order cores , and Core Fusion . Compared to a 2-way SMT out-of-order core , Morph Core increases performance by 10 % and reduces energy-delay-squared product by 22 % .
The topic has got a renewed interest after the recent irruption of Neural Machine Translation ( NMT ) , which is known to be particularly sensitive to the size and quality of the training data . This way , even if NMT has been shown to be vastly superior to Statistical Machine Translation ( SMT ) in standard benchmarks wu2016google , SMT still beats NMT when the training corpus is relatively small koehn2017six , and it is also substantially more robust to noisy training data khayrallah2018impact , thus increasing the need for large and high - quality parallel corpora .
In this paper , we investigate two supervised approaches for generating all types of artificial errors . We propose a framework for generating errors based on statistical machine translation ( SMT ) , training a model to translate from correct into incorrect sentences . In addition , we describe a method for learning error patterns from an annotated corpus and transplanting them into error - free text .
In this paper we compare error detection frameworks trained on the same publicly available FCE dataset , thereby removing the confounding factor of dataset size and only focusing on the model architectures . The error generation methods can generate alternative versions of the same input text - the pattern - based method randomly samples the error locations , and the SMT system can provide an n - best list of alternative translations . Therefore , we also investigated the combination of multiple error - generated versions of the input files when training error detection models .
Nevertheless , these early systems were later superseded by Statistical Machine Translation ( SMT ) based approaches , which induced an initial phrase - table through cross - lingual embedding mappings , combined it with an n - gram language model , and further improved the system through iterative back - translation lample2018phrase , artetxe2018usmt . In this paper , we develop a more principled approach to unsupervised SMT , addressing several deficiencies of previous systems by incorporating subword information , applying a theoretically well founded unsupervised tuning method , and developing a joint refinement procedure . In addition to that , we use our improved SMT approach to initialize an unsupervised NMT system , which is further improved through on - the - fly back - translation .
Results are shown in Table 8 . In phrasebased SMT , we find that the use of back - translated training data has a moderate positive effect on the WMT test sets ( + 0.7 BLEU ) , but not on the IWSLT test sets . This is in line with the expectation that the main effect of back - translated data for phrase - based SMT is domain adaptation [ reference ] .
This is by far the best result achieved by direct translation with large neural networks . For comparison , the BLEU score of an SMT baseline on this dataset is 33.30 . The 34.81 BLEU score was achieved by an LSTM with a vocabulary of 80k words , so the score was penalized whenever the reference translation contained a word not covered by these 80k .
In the field of statistical machine translation ( SMT ) , deep neural networks have begun to show promising results . summarizes a successful usage of feedforward neural networks in the framework of phrase - based SMT system . Along this line of research on using neural networks for SMT , this paper focuses on a novel neural network architecture that can be used as a part of the conventional phrase - based SMT system .
AMU14 and AMU16 : SMT - based GEC systems junczys2014amu , junczys2016phrase developed by AMU . NUS14 , NUS16 , NUS17 and
section : Related Work LSTM [ reference ] showed its early potentials in NLP when a neural machine translation system that leverages LSTM source encoding gave highly competitive results compared to the best SMT models [ reference ] . LSTM encoders have since been explored for other tasks , including syntactic parsing [ reference ] , text classification [ reference ] and machine reading
In this work , we use the Statistical Machine Translation approach . Statistical Machine Translation systems perform poorly when applied on new domains . The domain adaptation problem has recently gained interest in Statistical Machine Translation .
Our analysis shows the superiority of convolutional neural networks over recurrent neural networks such as long short-term memory ( LSTM ) networks in capturing the local context via attention , and thereby improving the coverage in correcting grammatical errors . By ensembling multiple models , and incorporating an N-gram language model and edit features via rescoring , our novel method becomes the first neural approach to outperform the current state-of-the-art statistical machine translation-based approach , both in terms of grammaticality and fluency .
At recent time , the statistical based language model and neural based language model are still dominating the researches in the field of machine translation . The statistical based machine translation today is the fastest one but it has a weakness in term of accuracy . In contrast , the neural based network has higher accuracy but has a very slow computation process .
Objective The objective of this work is to use scenario-based methods with EMS providers and nonnative English-speaking users who identified themselves as LEP ( henceforth referred to as LEP participants ) to evaluate the potential of two automated translation technologies in improving emergency communication . Methods We developed mock emergency scenarios and enacted them in simulation sessions with EMS personnel and Spanish-speaking and Chinese-speaking ( Mandarin ) LEP participants using two automated language translation tools : an EMS domain-specific fixed-sentence translation tool ( QuickSpeak ) and a statistical machine translation tool ( Google Translate ) . At the end of the sessions , we gathered feedback from both groups through a postsession questionnaire .
We describe the design of an autonomous agent that can teach itself how to translate from a foreign language , by first assembling its own training set , then using it to improve its vocabulary and language model . The key idea is that a Statistical Machine Translation package can be used for the Cross-Language Retrieval Task of assembling a training set from a vast amount of available text e.g. a large multilingual corpus , or the Web and then train on that data , repeating the process several times . The stability issues related to such a feedback loop are addressed by a mathematical model , connecting statistical and control-theoretic aspects of the system .
In order to cope with the issues , this work proposes a new model to correct spacing in multi-part words . The proposed method is based on statistical machine translation paradigm . In machine translation paradigm , text in source language is translated into a text in destination language on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora .
We adopt a unified approach to the problem . We frame the problem from a statistical Machine Translation perspective . We further post edit the output applying linguistically informed rules particular to the language pair and re-rank the output using machine learning methods .
A method based on statistical machine translation is presented that will enable robots to learn language . The proposed method is novel in two ways : ( 1 ) the language acquisition problem is formulated as a statistical machine translation problem , and ( 2 ) language-specific morphological analysis is not necessary . Promising experimental results were obtained by applying a traditional statistical machine translation method for sequence-to-sequence conversion .
We introduce a greedy algorithm called Local-(k , l)-Step , and show that it performs better than the DP-based algorithm . Our word-reordering algorithm can be used in the statistical machine translation process for improving the quality of the translation . Furthermore , motivated by the rank evaluation method , we introduce a novel way for evaluating the results of word-reordering by calculating the inversion pair cardinality .
An example of one-to-many mapping is Englishâ � � Indonesian preposition . This preposition translation mapping can be worked out using statistical method machine translation . The phrase-based method that embedded with the statistical method makes the machine translation become more powerful in handling preposition translation .
Recently , Sutskever et al . introduced a neural network model for solving the general sequence - to - sequence problem , and Bahdanau et al . proposed a related model with an attention mechanism that makes it capable of handling long sequences well . Both models achieve state - of - the - art results on large scale machine translation tasks ( e.g. , ) . Syntactic constituency parsing can be formulated as a sequence - to - sequence problem if we linearize the parse tree ( cf .
Neural Machine Translation ( NMT ) has obtained state - of - the art performance for several language pairs , while only using parallel data for training . Targetside monolingual data plays an important role in boosting fluency for phrasebased statistical machine translation , and we investigate the use of monolingual data for NMT . In contrast to previous work , which combines NMT models with separately trained language models , we note that encoder - decoder NMT architectures already have the capacity to learn the same information as a language model , and we explore strategies to train with monolingual data without changing the neural network architecture .
First , we summarize the research trends of papers presented in the proceedings . Second , we describe the results of the two shared tasks 1 ) efficient neural machine translation ( NMT ) where participants were tasked with creating NMT systems that are both accurate and efficient , and 2 ) document-level generation and translation ( DGT ) where participants were tasked with developing systems that generate summaries from structured data , potentially with assistance from text in another language .
In recent years , Neural Machine Translation ( NMT ) has been proven to get impressive results . While some additional linguistic features of input words improve wordlevel NMT , any additional character features have not been used to improve character-level NMT so far . In this paper , we show that the radicals of Chinese characters ( or kanji ) , as a character feature information , can be easily provide further improvements in the character-level NMT .
Also , pedestrians׳ preference of attributes in relation to different road classes highly agrees with current design principles . It was found that gender , age of pedestrian , purpose of trip and road class affect pedestrian preference and if considered in the planning and design of crossing facilities , they can result in improved safety , convenience and use of this Non-Motorised Transport ( NMT ) mode .
In this paper , we introduce a hybrid search for attention-based neural machine translation ( NMT ) . A target phrase learned with statistical MT models extends a hypothesis in the NMT beam search when the attention of the NMT model focuses on the source words translated by this phrase . Phrases added in this way are scored with the NMT model , but also with SMT features including phrase-level translation probabilities and a target language model .
It thus does not explicitly balance the contribution from word embeddings and the contribution from additional linguistic knowledge . To address these limitations , this paper proposes a knowledge-aware NMT approach that models additional linguistic features in parallel to the word feature . The core idea is that we propose modeling a series of linguistic features at the word level ( knowledge block ) using a recurrent neural network ( RNN ) .
Our solution requires no changes to the model architecture from a standard NMT system but instead introduces an artificial token at the beginning of the input sentence to specify the required target language . Using a shared wordpiece vocabulary , our approach enables Multilingual NMT systems using a single model . On the WMT’14 benchmarks , a single multilingual model achieves comparable performance for English→French and surpasses state-of-theart results for English→German .
An approach to boundary evaluation and a boundary representation are presented . The approach uses nonmanifold topology ( NMT ) to overcome editing and coverage limitations associated with the manifold nature of existing boundary evaluation schemes . It merges the input primitives into a single representation and selects the entities in the merged set that constitute the result of the Boolean operations .
paragraph : NLI as a supervised training set Our findings indicate that our model trained on SNLI obtains much better overall results than models trained on other supervised tasks such as COCO , dictionary definitions , NMT , PPDB and SST . For SST , we tried exactly the same models as for SNLI ; it is worth noting that SST is smaller than NLI .
section : Abstract Neural Machine Translation ( NMT ) has obtained state - of - the art performance for several language pairs , while only using parallel data for training . Targetside monolingual data plays an important role in boosting fluency for phrasebased statistical machine translation , and we investigate the use of monolingual data for NMT . In contrast to previous work , which combines NMT models with separately trained language models , we note that encoder - decoder NMT architectures already have the capacity to learn the same information as a language model , and we explore strategies to train with monolingual data without changing the neural network architecture . By pairing monolingual training data with an automatic backtranslation , we can treat it as additional parallel training data , and we obtain substantial improvements on the WMT 15 task English↔German
To obtain a synthetic parallel training set , we back - translate a random sample of 3 200 000 sentences from Gigaword . We use an English→Turkish NMT system trained with the same settings as the Turkish→English baseline system . We found overfitting to be a bigger problem than with the larger English↔German data set , and follow [ reference ] in using Gaussian noise ( stddev 0.01 ) [ reference ] , and dropout on the output layer ( p=0.5 )
ment of 2.9 BLEU . While it is unsurprising that in - domain parallel data is most valuable , we find it encouraging that NMT domain adaptation with monolingual data is also possible , and effective , since there are settings where only monolingual in - domain data is available . The best results published on this dataset are by , obtained with an ensemble of 8 independently trained models .
In addition to that , we show that our method can also bring large improvements to downstream machine translation . An NMT model trained on our filtered version of ParaCrawl obtains 31.2 BLEU points on the standard English - German newstest2014 benchmark , an improvement of more than one point over an equivalent system trained on the official filtered version . section : Related work
Several architectures have been proposed to reduce the computational load associated with recurrence - based computation gehring2016convenc , gehring2017convs2s , kaiser2016can , kalchbrenner2016neural . Self - attention , which relies on dot - products between elements of the input sequence to compute a weighted sum lin2017structured , bahdanau2014neural , parikh2016decomposable , kim2017structured , has also been a critical ingredient in modern NMT architectures . The Transformer network vaswani2017attention avoids the recurrence completely and uses only self - attention .
Adapted - transformer : a transformer vaswani2017attention based GEC system junczys2018approaching with techniques adapted from low - resource machine translation . SMT - NMT hybrid : the state - of - the - art GEC system grundkiewicz2018near that is based on an SMT - NMT hybrid approach . Table [ reference ] shows the results of GEC systems on CoNLL and JFLEG dataset .
We apply dropout to the output of each LSTM layer in the decoder . Our implementation is heavily based off of the Google NMT Tutorial luong17 . We attribute our significantly better results to using pre - trained word embeddings , a character - level CNN , a larger model , stronger regularization , and better hyperparameter tuning .
Compared with a heuristic similarity based method , the proposed method significantly improves the performance with only limited sub-tree aligned data . To examine its effectiveness for multilingual applications , we further attempt different approaches to apply the sub-tree alignment in both phrase and syntax based SMT systems . We then compare the performance with that of the widely used word alignment .
section : Contrast to Phrase - based SMT The back - translation of monolingual target data into the source language to produce synthetic parallel text has been previously explored for phrase - based SMT [ reference ][ reference ] .
Our best results are obtained with an ensemble of LSTMs that differ in their random initializations and in the random order of minibatches . While the decoded translations of the LSTM ensemble do not outperform the best WMT’14 system , it is the first time that a pure neural translation system outperforms a phrase - based SMT baseline on a large scale MT task by a sizeable margin , despite its inability to handle out - of - vocabulary words . The LSTM is within 0.5 BLEU points of the best WMT’14 result if it is used to rescore the 1000 - best list of the baseline system .
The reverse of this idea , translating from incorrect to correct sentences , has been shown to work well for error correction tasks [ reference ][ reference ] , and roundtrip translation has also been shown to be promising for correcting grammatical errors [ reference ] . Following previous work [ reference ][ reference ] , we build a phrase - based SMT error generation system . During training , error - corrected sentences in the training data are treated as the source , and the original sentences written by language learners as the target .
Section [ reference ] first discusses the related work in the topic . Section [ reference ] then describes our principled unsupervised SMT method , while Section [ reference ] discusses our hybridization method with NMT . We then present the experiments done and the results obtained in Section [ reference ] , and Section [ reference ] concludes the paper .
영어 특허문서 자동번역을 위한 특허번역패턴 연구 The Japanese Government Project for Machine Translation Integration of a Multilingual Preordering Component into a Commercial SMT Platform The Design and Implementation of the Frame of Machine-based English-Chinese Translation System An automatic non-native speaker recognition system Availability of Multilingual Chat Communication in 3D Online Virtual Space Electronic Tools for Translators A Review on Machine Translation Approaches Design and application of translation robot based on knowledge management Application of Pronominal Divergence and Anaphora Resolution in English-Hindi Machine Translation Attention is All you Need Faithful to the Original: Fact Aware Neural Abstractive Summarization Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study Image Captioning and Visual Question Answering Based on Attributes and External Knowledge An Effective Approach to Unsupervised Machine Translation Graph Attention Networks Sequence Level Training with Recurrent Neural Networks Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer Resolution proof transformation for compression and interpolation Neural System Combination for Machine Translation Understanding prediction-based partial redundant threading for low-overhead, high- coverage fault tolerance Automatic Post-Editing for the DiscoMT Pronoun Translation Task Robustness Verification for Classifier Ensembles Good is good A novel dependency based word-level reordering model for phrased-based translation MorphCore: An Energy-Efficient Microarchitecture for High Performance ILP and High Throughput TLP Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings Artificial Error Generation with Machine Translation and Syntactic Patterns An Effective Approach to Unsupervised Machine Translation Improving Neural Machine Translation Models with Monolingual Data Sequence to Sequence Learning with Neural Networks Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study Sentence-State LSTM for Text Representation Domain adaptation problem in statistical machine translation systems A Multilayer Convolutional Encoder-Decoder Neural Network for Grammatical Error Correction Recurrent neural network language model for English-Indonesian Machine Translation: Experimental study Evaluating the Usefulness of Translation Technologies for Emergency Response Communication: A Scenario-Based Study An intelligent Web agent that autonomously learns how to translate A new model for persian multi-part words edition based on statistical machine translation Named Entity Transliteration Generation Leveraging Statistical Machine Translation Technology A language acquisition method based on statistical machine translation for application to robots Word-reordering for Statistical Machine Translation Using Trigram Language Model ALGORITMA MESIN PENERJEMAH BERBASISSTATISTIK MENGGUNAKAN METODE LINKGRAMMAR UNTUK FRASE PREPOSISIONAL Grammar as a Foreign Language Improving Neural Machine Translation Models with Monolingual Data Findings of the Third Workshop on Neural Generation and Translation Improving character-level Japanese-Chinese neural machine translation with radicals as an additional input feature Implication of pedestrians' stated preference of certain attributes of crosswalks Neural Machine Translation Leveraging Phrase-based Models in a Hybrid Search Linguistic Knowledge-Aware Neural Machine Translation Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation An editable nonmanifold boundary representation Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Improving Neural Machine Translation Models with Monolingual Data Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings Weighted Transformer Network for Machine Translation Reaching Human-level Performance in Automatic Grammatical Error Correction: An Empirical Study Semi-Supervised Sequence Modeling with Cross-View Training Discriminative Induction of Sub-Tree Alignment using Limited Labeled Data Improving Neural Machine Translation Models with Monolingual Data Sequence to Sequence Learning with Neural Networks Artificial Error Generation with Machine Translation and Syntactic Patterns An Effective Approach to Unsupervised Machine Translation
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Culture and Computing NIPS AAAI ArXiv IEEE Transactions on Pattern Analysis and Machine Intelligence ACL ICLR ICLR ICLR ASPLOS DiscoMT@EMNLP SIGGRAPH Computer Animation Festival NLPKE MICRO ACL BEA@EMNLP ACL ACL NIPS EMNLP ArXiv ACL CCIA NEWS@IJCNLP ICDL-EPIROB IJCNLP NIPS ACL NGT@EMNLP-IJCNLP EMNLP ACL ACL ArXiv ArXiv EMNLP COLING ACL NIPS BEA@EMNLP ACL
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