Purpose of this paper is to present the experience and findings of international collaborative research conducted over several years on vision-based sign language translation . At first , this paper extends a discussion on sign language and its variations , machine-based translation and its significance . Secondly , a discussion on how three main tasks within the machine translation , namely : a ) hand localization and tracking , b ) hand posture interpretation , and c ) hand gesture interpretation , can be addressed . The objective of this research is to find out the potentiality of machine based translation , such as Google Translation for example , in translating fixed expressions . The framework for fixed expression will be based on Baker ’s typology of equivalence ( 1992 ) and Alexander ’s classification ( 1978 ) . Machine language translation as it stands today relies primarily on rule-based methods , which use a direct dictionary translation and at best attempts to rearrange the words in a sentence to follow the translation language ’s grammar rules to allow for better parsing on the part of the user . This project seeks to implement a rule-based translation from German to English , for users who are only fluent in one of the languages . Warnings about the display of signs are important in several areas of application : machine language translation , text-to-speech , help for visually impaired and foreigners . There are some methods and are described in the literature . * * ) in patent Chinese-English machine translation . In machine translation , Ηe ( * * * ) mainly acts as noun , preposition and conjunction for patent text . We formulate the problem of image captioning as a multimodal translation task . Analogous to machine translation , we present a sequence-to-sequence recurrent neural networks ( RNN ) model for image caption generation . Different from most existing work where the whole image is represented by a convolutional neural networks ( CNN ) feature , we propose to represent the input image as a sequence of detected objects to serve as the source sequence of the RNN model . The paper focuses on advantages and disadvantages of translation ICT tools : translation databases ( Reverso context ) ; electronic dictionaries ( ABBYY Lingvо , multitran ) necessary for improving and expanding would-be translators ’ vocabulary ; electronic text corpora as sources of authentic linguistic materials , machine translation systems ( Google Translate ) and CAT tools ( Trados , SmartCAT ) capable of accelerating target text drafting . In the context of machine translation analysis , the necessity to determine the indicators of machine translatability and to carry out source text pre-editing based on controlled language rules and target text post-editing is emphasized . It is pointed out that CAT tools can increase translators ’ performance , provide terminology consistency and enable translation project management ; at the same time , their application requires developing special skills . 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 This paper studies the impact that difficult-to-translate source-language phrases might have on the machine translation process . We formulate the notion of difficulty as a measurable quantity ; we show that a classifier can be trained to predict whether a phrase might be difficult to translate ; and we develop a framework that makes use of the classifier and external resources ( such as human translators ) to improve the overall translation quality . The requirements made of the target language were that it be parenthesisfree , that it contain a minimum number of instruction types , and that it would simplify the translation process . The feasibility of this language as target language to simplify the machine translation process and the direct implementation of this language on a computer by means of suitable hardware is considered . ( C.H. ) We have submitted run for Japanese ( JA ) System Validation ( one run BC and one for MC ) , Chinese Simplified ( CS ) System Validation ( one run ) . The Textual Entailment system used the web based Google translator system 2 for Machine Translation purpose . The system is based on Support Vector Machine that uses features from lexical similarity , lexical distance , and syntactic similarity . Resource Implementation . Abstract This paper discusses the first steps undertaken to create necessary linguistic resources to incorporate Amazigh language within the Universal Networking Language ( UNL ) framework for machine translation purpose . This universal interlanguage allows to any source text to be translated into different other related languages with UNL by converting the meaning of the source text into semantic graph . 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 . 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 . Neural networks augmented with external memory have the ability to learn algorithmic solutions to complex tasks . These models appear promising for applications such as language modeling and machine translation . However , they scale poorly in both space and time as the amount of memory grows — limiting their applicability to real - world domains . 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 . Transliterating names between Sinhala , Tamil and English . In this paper , we focus on building models for transliteration of personal names between the primary languages of Sri Lanka-namely Sinhala , Tamil and English . Currently , a Rule-based system has been used to transliterate names between Sinhala and Tamil . It is undoubtedly the unique task that has been done for the first time . Utmost care has been taken for the accuracy and pronunciation of transliteration . This is a continuous research work , and we aim to update the community with more progress in future in other areas of knowledge retrieval of Holy Quran . Language transliteration is one of the important area in natural language processing . Accurate transliteration of named entities plays an important role in the performance of machine translation and cross-language information retrieval processes . The transliteration model must be design in such a way that the phonetic structure of words should be preserve as closely as possible . Our models outperform a widely used word aligner on 97 % of test words , showing the particular efficacy of our approach on the impactful task of broadly multilingual named-entity alignment and translation across a remarkably large number of world languages . We further illustrate the utility of our translation matrix for the multilingual learning of name-related affixes and their semantics as well as transliteration of named entities . The abundance of homophones in Chinese significantly increases the number of similarly acceptable candidates in English-to-Chinese transliteration ( E2C ) . The dialectal factor also leads to different transliteration practice . The bilingual lexicon containing aligned segments of various syllable lengths subsequently allows direct transliteration by chunks . The official results suggest that our system could potentially be improved with a re-ranking module for English-to-Chinese transliteration , while its performance on Chinese-to-English back transliteration reached the state of the art . This paper describes work aimed at using the widely applied graphical models approach of ‘ Dynamic Bayesian Networks ( DBNs ) to transliteration identification . The task of estimating transliteration similarity is not very different from specific identification tasks where DBNs have been successfully applied ; it is also possible to adapt DBN models from the other identification domains to the transliteration identification domain . In particular , we investigate the applicability of a DBN framework initially proposed by Filali and Bilmes ( 2005 ) to learn edit distance estimation parameters for use in pronunciation classification . In this paper we investigate Chinese-English name transliteration using comparable corpora , corpora where texts in the two languages deal in some of the same topics --- and therefore share references to named entities --- but are not translations of each other . We present two distinct methods for transliteration , one approach using phonetic transliteration , and the second using the temporal distribution of candidate pairs . Das Arbeitsprogramm des Normungskomitees umfasste zunächst u. a. Themen wie die Präsentation von Zeitschriften , die Transliteration von kyrillischen Buchstaben , die Größe und die Qualität von Mikroformen , die Lesegeräte von Mikroformen und die Terminologie der Information und Dokumentation . These texts are characterized by the use of code-switching between the Modern Standard Arabic ( MSA ) and the Arabic Dialect ( AD ) in the texts written in Arabic , or between Arabizi and foreign languages for those written in Latin . This paper presents the specific resources and tools we have developed for this purpose , with a focus on the transliteration of Arabizi into Arabic ( using the dedicated tools for Arabic dialects ) . A dictionary-based approach to detect the dialectal origin of a text is described , it exhibits satisfactory results . Our methodology is fully bi-directional , i.e. the same methodology is used for both transliteration and back-transliteration . For the machine learning of transliteration and back-transliteration rules , many phonetically aligned English word and Korean transliteration pairs are needed . However , such aligned pairs are not generally available so that automatic alignment is necessary . We discover that intrinsic evaluation , i.e comparison to a single gold standard , might not be appropriate in the task of transliteration due to its high variability . For this reason , we propose using extrinsic evaluation of transliteration via the cross-lingual named entity list search task ( e.g. personal name search in contacts list ) . Our code and datasets are publicly available online . Also , word - level models are unable to translate or generate unseen words . Copying unknown words into the target text , as done by [ reference ][ reference ] , is a reasonable strategy for names , but morphological changes and transliteration is often required , especially if alphabets differ . We investigate NMT models that operate on the level of subword units . The English→Russian examples show that the subword systems are capable of transliteration . However , transliteration errors do occur , either due to ambiguous transliterations , or because of non - consistent segmentations between source and target text which make it hard for the system to learn a transliteration mapping . Note that the BPE - 60k system encodes Mirzayeva inconsistently for the two language pairs ( Mirz|ayeva→Мир|за|ева Mir|za|eva ) . On NewsTest 2015 , to our knowledge , ByteNet achieves the best published results to date . Table [ reference ] contains some of the unaltered generated translations from the ByteNet that highlight reordering and other phenomena such as transliteration . The character - level aspect of the model makes post - processing unnecessary in principle . Our experiments demonstrate that our method works very well also for distant language pairs , like English-Russian or English-Chinese . We finally describe experiments on the English-Esperanto low-resource language pair , on which there only exists a limited amount of parallel data , to show the potential impact of our method in fully unsupervised machine translation . Our code , embeddings and dictionaries are publicly available . The proposed data-dependent Gaussian prior objective ( D2GPo ) is defined over a prior topological order of tokens , poles apart from the data-independent Gaussian prior ( L2 regularization ) commonly adopted for smoothing the training of MLE . Experimental results show that the proposed method can effectively make use of more detailed prior in the data and significantly improve the performance of typical language generation tasks , including supervised and unsupervised machine translation , text summarization , storytelling , and image caption . In particular , we show that we can perform word translation without parallel data , which was the building block for the new research field of " unsupervised machine translation " . My last contribution on cross-lingual language modeling shows that state-of-the-art sentence representations can be aligned in a completely unsupervised way , leading to a new state of the art on supervised and unsupervised machine translation , and on the zero-shot crosslingual classification benchmarked called " XNLI " . On unsupervised machine translation , we obtain 34.3 BLEU on WMT’16 German-English , improving the previous state of the art by more than 9 BLEU . On supervised machine translation , we obtain a new state of the art of 38.5 BLEU on WMT’16 Romanian-English , outperforming the previous best approach by more than 4 BLEU . Our code and pretrained models will be made publicly available . CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples , achieving the task of unsupervised data translation . However , there is no theoretical guarantee on the property of the learned one-to-one mapping in CycleGAN . We frame unsupervised machine translation ( MT ) in the context of multi-task learning ( MTL ) , combining insights from both directions . We leverage off-the-shelf neural MT architectures to train unsupervised MT models with no parallel data and show that such models can achieve reasonably good performance , competitive with models purpose-built for unsupervised MT . Despite the reported success of unsupervised machine translation ( MT ) , the field has yet to examine the conditions under which these methods succeed , and where they fail . We conduct an extensive empirical evaluation of unsupervised MT using dissimilar language pairs , dissimilar domains , diverse datasets , and authentic low-resource languages . In addition , RS improves the quality of bilingual dictionaries by measuring the bilateral similarity of the bilingual and monolingual distance of each of the source query embeddings . Additionally , to model the unsupervised machine word translation , we introduce Generative Adversarial Nets ( GANs ) to map the source and target word distribution into a shared semantic space . We also construct a tiny GAN topology for neural machine word translation , which is at least 52 × faster than previous GAN-based models . The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs . Back-translation has been dominantly used in previous approaches for unsupervised neural machine translation , where pseudo sentence pairs are generated to train the models with a reconstruction loss . However , the pseudo sentences are usually of low quality as translation errors accumulate during training . In this paper , we alleviate the local optimality of back-translation by learning a policy ( takes the form of an encoder-decoder and is defined by its parameters ) with future rewarding under the reinforcement learning framework , which aims to optimize the global word predictions for unsupervised neural machine translation . To this end , we design a novel reward function to characterize high-quality translations from two aspects : n-gram matching and semantic adequacy . We train our model using monolingual data only from both languages . The final model is an unsupervised neural model using established techniques for unsupervised translation such as denoising autoencoding and online back-translation . We bootstrap the model with masked language model pretraining and enhance it with back-translations from an unsupervised phrase-based system which is itself bootstrapped using unsupervised bilingual word embeddings . Unsupervised cross-lingual embeddings mapping has provided a unique tool for completely unsupervised translation even for languages with different scripts . In this work we use this method for the task of unsupervised cross-lingual matching of product classifications . As it can be seen , our unsupervised system outperforms the WMT 2014 shared task winner in English - to - German , and is around 2 BLEU points behind it in the other translation directions . This shows that unsupervised machine translation is already competitive with the state - of - the - art in supervised machine translation in 2014 . While the field of machine translation has undergone great progress in the last 5 years , and the gap between our unsupervised system and the current state - of - the - art in supervised machine translation is still large as reflected by the other results , this suggests that unsupervised machine translation can be a usable alternative in practical settings . Due to the lack of structured language resources , low-resource language machine translation often faces difficulties in cross-language semantic paraphrasing . In order to solve the problem of low-resource machine translation from Indonesian to Chinese , a cognate-parallel-corpus-based expanding method of language resources is proposed , and an improved neural machine translation model is trained by the Malay-corpus-enhanced corpus . The improved model can achieve a comparable result as that of Google in the experiment of Indonesian-Chinese machine translation . Significant part of presented results was obtained during DeepHack . Babel hackathon on low-resource machine translation organized by iPavlov Lab . To learn the parameters of our model , we use cross-lingual supervision , hypothesizing that a good representation of a word in context will be one that is sufficient for selecting the correct translation into a second language . We evaluate the quality of our representations as features in three downstream tasks : prediction of semantic supersenses ( which assign nouns and verbs into a few dozen semantic classes ) , low resource machine translation , and a lexical substitution task , and obtain state-of-the-art results on all of these . Meta-evaluation is a method to assess machine translation ( MT ) evaluation metrics according to certain theories and standards . This paper addresses an automatic meta-evaluation method of machine translation evaluation based on ORANGE- Limited ORANGE , which is applied in low-resource machine translation evaluation . It is adopted when the resources are limited . Although many techniques take advantage of a seed bilingual lexicon , this work is the first to use that data for supervised learning to combine a diverse set of signals derived from a pair of monolingual corpora into a single discriminative model . Even in a low resource machine translation setting , where induced translations have the potential to improve performance substantially , it is reasonable to assume access to some amount of data to perform this kind of optimization . Our work shows that only a few hundred translation pairs are needed to achieve strong performance on the bilingual lexicon induction task , and our approach yields an average relative gain in accuracy of nearly 50 % over an unsupervised baseline . The second approach applies cross-lingual embedding and source sentence representation space sharing to strengthen the encoder of low-resource NMT . Our experiments demonstrate that the proposed method can obtain significant improvements on low-resource neural machine translation than baseline system . On the IWSLT2015 Vietnamese-English translation task , our model can improve the translation quality by an average of 1.43 BLEU scores . The Case of Low-Resource Neural Machine Translation . Neural Machine Translation ( NMT ) , a datahungry technology , suffers from the lack of bilingual data in low-resource scenarios . In this work , we explore massively multilingual low-resource neural machine translation . Using translations of the Bible ( which have parallel structure across languages ) , we train models with up to 1,107 source languages . Benchmarks on machine translation and speech recognition tasks show that models built using OpenSeq2Seq give state-of-the-art performance at 1.5 - 3x less training time . OpenSeq2Seq currently provides building blocks for models that solve a wide range of tasks including neural machine translation , automatic speech recognition , and speech synthesis . Recent works in neural machine translation have begun to explore document translation . However , translating online multi-speaker conversations is still an open problem . Structural planning is important for producing long sentences , which is a missing part in current language generation models . In this work , we add a planning phase in neural machine translation to control the coarse structure of output sentences . The model first generates some planner codes , then predicts real output words conditioned on them . document : Non - Autoregressive Neural Machine Translation Existing approaches to neural machine translation condition each output word on previously generated outputs . We introduce a model that avoids this autoregressive property and produces its outputs in parallel , allowing an order of magnitude lower latency during inference .
Purpose of this paper is to present the experience and findings of international collaborative research conducted over several years on vision-based sign language translation . At first , this paper extends a discussion on sign language and its variations , machine-based translation and its significance . Secondly , a discussion on how three main tasks within the machine translation , namely : a ) hand localization and tracking , b ) hand posture interpretation , and c ) hand gesture interpretation , can be addressed .
The objective of this research is to find out the potentiality of machine based translation , such as Google Translation for example , in translating fixed expressions . The framework for fixed expression will be based on Baker ’s typology of equivalence ( 1992 ) and Alexander ’s classification ( 1978 ) .
Machine language translation as it stands today relies primarily on rule-based methods , which use a direct dictionary translation and at best attempts to rearrange the words in a sentence to follow the translation language ’s grammar rules to allow for better parsing on the part of the user . This project seeks to implement a rule-based translation from German to English , for users who are only fluent in one of the languages .
Warnings about the display of signs are important in several areas of application : machine language translation , text-to-speech , help for visually impaired and foreigners . There are some methods and are described in the literature .
* * ) in patent Chinese-English machine translation . In machine translation , Ηe ( * * * ) mainly acts as noun , preposition and conjunction for patent text .
We formulate the problem of image captioning as a multimodal translation task . Analogous to machine translation , we present a sequence-to-sequence recurrent neural networks ( RNN ) model for image caption generation . Different from most existing work where the whole image is represented by a convolutional neural networks ( CNN ) feature , we propose to represent the input image as a sequence of detected objects to serve as the source sequence of the RNN model .
The paper focuses on advantages and disadvantages of translation ICT tools : translation databases ( Reverso context ) ; electronic dictionaries ( ABBYY Lingvо , multitran ) necessary for improving and expanding would-be translators ’ vocabulary ; electronic text corpora as sources of authentic linguistic materials , machine translation systems ( Google Translate ) and CAT tools ( Trados , SmartCAT ) capable of accelerating target text drafting . In the context of machine translation analysis , the necessity to determine the indicators of machine translatability and to carry out source text pre-editing based on controlled language rules and target text post-editing is emphasized . It is pointed out that CAT tools can increase translators ’ performance , provide terminology consistency and enable translation project management ; at the same time , their application requires developing special skills .
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
This paper studies the impact that difficult-to-translate source-language phrases might have on the machine translation process . We formulate the notion of difficulty as a measurable quantity ; we show that a classifier can be trained to predict whether a phrase might be difficult to translate ; and we develop a framework that makes use of the classifier and external resources ( such as human translators ) to improve the overall translation quality .
The requirements made of the target language were that it be parenthesisfree , that it contain a minimum number of instruction types , and that it would simplify the translation process . The feasibility of this language as target language to simplify the machine translation process and the direct implementation of this language on a computer by means of suitable hardware is considered . ( C.H. )
We have submitted run for Japanese ( JA ) System Validation ( one run BC and one for MC ) , Chinese Simplified ( CS ) System Validation ( one run ) . The Textual Entailment system used the web based Google translator system 2 for Machine Translation purpose . The system is based on Support Vector Machine that uses features from lexical similarity , lexical distance , and syntactic similarity .
Resource Implementation . Abstract This paper discusses the first steps undertaken to create necessary linguistic resources to incorporate Amazigh language within the Universal Networking Language ( UNL ) framework for machine translation purpose . This universal interlanguage allows to any source text to be translated into different other related languages with UNL by converting the meaning of the source text into semantic graph .
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 .
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 .
Neural networks augmented with external memory have the ability to learn algorithmic solutions to complex tasks . These models appear promising for applications such as language modeling and machine translation . However , they scale poorly in both space and time as the amount of memory grows — limiting their applicability to real - world domains .
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 .
Transliterating names between Sinhala , Tamil and English . In this paper , we focus on building models for transliteration of personal names between the primary languages of Sri Lanka-namely Sinhala , Tamil and English . Currently , a Rule-based system has been used to transliterate names between Sinhala and Tamil .
It is undoubtedly the unique task that has been done for the first time . Utmost care has been taken for the accuracy and pronunciation of transliteration . This is a continuous research work , and we aim to update the community with more progress in future in other areas of knowledge retrieval of Holy Quran .
Language transliteration is one of the important area in natural language processing . Accurate transliteration of named entities plays an important role in the performance of machine translation and cross-language information retrieval processes . The transliteration model must be design in such a way that the phonetic structure of words should be preserve as closely as possible .
Our models outperform a widely used word aligner on 97 % of test words , showing the particular efficacy of our approach on the impactful task of broadly multilingual named-entity alignment and translation across a remarkably large number of world languages . We further illustrate the utility of our translation matrix for the multilingual learning of name-related affixes and their semantics as well as transliteration of named entities .
The abundance of homophones in Chinese significantly increases the number of similarly acceptable candidates in English-to-Chinese transliteration ( E2C ) . The dialectal factor also leads to different transliteration practice .
The bilingual lexicon containing aligned segments of various syllable lengths subsequently allows direct transliteration by chunks . The official results suggest that our system could potentially be improved with a re-ranking module for English-to-Chinese transliteration , while its performance on Chinese-to-English back transliteration reached the state of the art .
This paper describes work aimed at using the widely applied graphical models approach of ‘ Dynamic Bayesian Networks ( DBNs ) to transliteration identification . The task of estimating transliteration similarity is not very different from specific identification tasks where DBNs have been successfully applied ; it is also possible to adapt DBN models from the other identification domains to the transliteration identification domain . In particular , we investigate the applicability of a DBN framework initially proposed by Filali and Bilmes ( 2005 ) to learn edit distance estimation parameters for use in pronunciation classification .
In this paper we investigate Chinese-English name transliteration using comparable corpora , corpora where texts in the two languages deal in some of the same topics --- and therefore share references to named entities --- but are not translations of each other . We present two distinct methods for transliteration , one approach using phonetic transliteration , and the second using the temporal distribution of candidate pairs .
Das Arbeitsprogramm des Normungskomitees umfasste zunächst u. a. Themen wie die Präsentation von Zeitschriften , die Transliteration von kyrillischen Buchstaben , die Größe und die Qualität von Mikroformen , die Lesegeräte von Mikroformen und die Terminologie der Information und Dokumentation .
These texts are characterized by the use of code-switching between the Modern Standard Arabic ( MSA ) and the Arabic Dialect ( AD ) in the texts written in Arabic , or between Arabizi and foreign languages for those written in Latin . This paper presents the specific resources and tools we have developed for this purpose , with a focus on the transliteration of Arabizi into Arabic ( using the dedicated tools for Arabic dialects ) . A dictionary-based approach to detect the dialectal origin of a text is described , it exhibits satisfactory results .
Our methodology is fully bi-directional , i.e. the same methodology is used for both transliteration and back-transliteration . For the machine learning of transliteration and back-transliteration rules , many phonetically aligned English word and Korean transliteration pairs are needed . However , such aligned pairs are not generally available so that automatic alignment is necessary .
We discover that intrinsic evaluation , i.e comparison to a single gold standard , might not be appropriate in the task of transliteration due to its high variability . For this reason , we propose using extrinsic evaluation of transliteration via the cross-lingual named entity list search task ( e.g. personal name search in contacts list ) . Our code and datasets are publicly available online .
Also , word - level models are unable to translate or generate unseen words . Copying unknown words into the target text , as done by [ reference ][ reference ] , is a reasonable strategy for names , but morphological changes and transliteration is often required , especially if alphabets differ . We investigate NMT models that operate on the level of subword units .
The English→Russian examples show that the subword systems are capable of transliteration . However , transliteration errors do occur , either due to ambiguous transliterations , or because of non - consistent segmentations between source and target text which make it hard for the system to learn a transliteration mapping . Note that the BPE - 60k system encodes Mirzayeva inconsistently for the two language pairs ( Mirz|ayeva→Мир|за|ева Mir|za|eva ) .
On NewsTest 2015 , to our knowledge , ByteNet achieves the best published results to date . Table [ reference ] contains some of the unaltered generated translations from the ByteNet that highlight reordering and other phenomena such as transliteration . The character - level aspect of the model makes post - processing unnecessary in principle .
Our experiments demonstrate that our method works very well also for distant language pairs , like English-Russian or English-Chinese . We finally describe experiments on the English-Esperanto low-resource language pair , on which there only exists a limited amount of parallel data , to show the potential impact of our method in fully unsupervised machine translation . Our code , embeddings and dictionaries are publicly available .
The proposed data-dependent Gaussian prior objective ( D2GPo ) is defined over a prior topological order of tokens , poles apart from the data-independent Gaussian prior ( L2 regularization ) commonly adopted for smoothing the training of MLE . Experimental results show that the proposed method can effectively make use of more detailed prior in the data and significantly improve the performance of typical language generation tasks , including supervised and unsupervised machine translation , text summarization , storytelling , and image caption .
In particular , we show that we can perform word translation without parallel data , which was the building block for the new research field of " unsupervised machine translation " . My last contribution on cross-lingual language modeling shows that state-of-the-art sentence representations can be aligned in a completely unsupervised way , leading to a new state of the art on supervised and unsupervised machine translation , and on the zero-shot crosslingual classification benchmarked called " XNLI " .
On unsupervised machine translation , we obtain 34.3 BLEU on WMT’16 German-English , improving the previous state of the art by more than 9 BLEU . On supervised machine translation , we obtain a new state of the art of 38.5 BLEU on WMT’16 Romanian-English , outperforming the previous best approach by more than 4 BLEU . Our code and pretrained models will be made publicly available .
CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples , achieving the task of unsupervised data translation . However , there is no theoretical guarantee on the property of the learned one-to-one mapping in CycleGAN .
We frame unsupervised machine translation ( MT ) in the context of multi-task learning ( MTL ) , combining insights from both directions . We leverage off-the-shelf neural MT architectures to train unsupervised MT models with no parallel data and show that such models can achieve reasonably good performance , competitive with models purpose-built for unsupervised MT .
Despite the reported success of unsupervised machine translation ( MT ) , the field has yet to examine the conditions under which these methods succeed , and where they fail . We conduct an extensive empirical evaluation of unsupervised MT using dissimilar language pairs , dissimilar domains , diverse datasets , and authentic low-resource languages .
In addition , RS improves the quality of bilingual dictionaries by measuring the bilateral similarity of the bilingual and monolingual distance of each of the source query embeddings . Additionally , to model the unsupervised machine word translation , we introduce Generative Adversarial Nets ( GANs ) to map the source and target word distribution into a shared semantic space . We also construct a tiny GAN topology for neural machine word translation , which is at least 52 × faster than previous GAN-based models .
The overreliance on large parallel corpora significantly limits the applicability of machine translation systems to the majority of language pairs . Back-translation has been dominantly used in previous approaches for unsupervised neural machine translation , where pseudo sentence pairs are generated to train the models with a reconstruction loss . However , the pseudo sentences are usually of low quality as translation errors accumulate during training .
In this paper , we alleviate the local optimality of back-translation by learning a policy ( takes the form of an encoder-decoder and is defined by its parameters ) with future rewarding under the reinforcement learning framework , which aims to optimize the global word predictions for unsupervised neural machine translation . To this end , we design a novel reward function to characterize high-quality translations from two aspects : n-gram matching and semantic adequacy .
We train our model using monolingual data only from both languages . The final model is an unsupervised neural model using established techniques for unsupervised translation such as denoising autoencoding and online back-translation . We bootstrap the model with masked language model pretraining and enhance it with back-translations from an unsupervised phrase-based system which is itself bootstrapped using unsupervised bilingual word embeddings .
Unsupervised cross-lingual embeddings mapping has provided a unique tool for completely unsupervised translation even for languages with different scripts . In this work we use this method for the task of unsupervised cross-lingual matching of product classifications .
As it can be seen , our unsupervised system outperforms the WMT 2014 shared task winner in English - to - German , and is around 2 BLEU points behind it in the other translation directions . This shows that unsupervised machine translation is already competitive with the state - of - the - art in supervised machine translation in 2014 . While the field of machine translation has undergone great progress in the last 5 years , and the gap between our unsupervised system and the current state - of - the - art in supervised machine translation is still large as reflected by the other results , this suggests that unsupervised machine translation can be a usable alternative in practical settings .
Due to the lack of structured language resources , low-resource language machine translation often faces difficulties in cross-language semantic paraphrasing . In order to solve the problem of low-resource machine translation from Indonesian to Chinese , a cognate-parallel-corpus-based expanding method of language resources is proposed , and an improved neural machine translation model is trained by the Malay-corpus-enhanced corpus . The improved model can achieve a comparable result as that of Google in the experiment of Indonesian-Chinese machine translation .
Significant part of presented results was obtained during DeepHack . Babel hackathon on low-resource machine translation organized by iPavlov Lab .
To learn the parameters of our model , we use cross-lingual supervision , hypothesizing that a good representation of a word in context will be one that is sufficient for selecting the correct translation into a second language . We evaluate the quality of our representations as features in three downstream tasks : prediction of semantic supersenses ( which assign nouns and verbs into a few dozen semantic classes ) , low resource machine translation , and a lexical substitution task , and obtain state-of-the-art results on all of these .
Meta-evaluation is a method to assess machine translation ( MT ) evaluation metrics according to certain theories and standards . This paper addresses an automatic meta-evaluation method of machine translation evaluation based on ORANGE- Limited ORANGE , which is applied in low-resource machine translation evaluation . It is adopted when the resources are limited .
Although many techniques take advantage of a seed bilingual lexicon , this work is the first to use that data for supervised learning to combine a diverse set of signals derived from a pair of monolingual corpora into a single discriminative model . Even in a low resource machine translation setting , where induced translations have the potential to improve performance substantially , it is reasonable to assume access to some amount of data to perform this kind of optimization . Our work shows that only a few hundred translation pairs are needed to achieve strong performance on the bilingual lexicon induction task , and our approach yields an average relative gain in accuracy of nearly 50 % over an unsupervised baseline .
The second approach applies cross-lingual embedding and source sentence representation space sharing to strengthen the encoder of low-resource NMT . Our experiments demonstrate that the proposed method can obtain significant improvements on low-resource neural machine translation than baseline system . On the IWSLT2015 Vietnamese-English translation task , our model can improve the translation quality by an average of 1.43 BLEU scores .
The Case of Low-Resource Neural Machine Translation . Neural Machine Translation ( NMT ) , a datahungry technology , suffers from the lack of bilingual data in low-resource scenarios .
In this work , we explore massively multilingual low-resource neural machine translation . Using translations of the Bible ( which have parallel structure across languages ) , we train models with up to 1,107 source languages .
Benchmarks on machine translation and speech recognition tasks show that models built using OpenSeq2Seq give state-of-the-art performance at 1.5 - 3x less training time . OpenSeq2Seq currently provides building blocks for models that solve a wide range of tasks including neural machine translation , automatic speech recognition , and speech synthesis .
Recent works in neural machine translation have begun to explore document translation . However , translating online multi-speaker conversations is still an open problem .
Structural planning is important for producing long sentences , which is a missing part in current language generation models . In this work , we add a planning phase in neural machine translation to control the coarse structure of output sentences . The model first generates some planner codes , then predicts real output words conditioned on them .
document : Non - Autoregressive Neural Machine Translation Existing approaches to neural machine translation condition each output word on previously generated outputs . We introduce a model that avoids this autoregressive property and produces its outputs in parallel , allowing an order of magnitude lower latency during inference .
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HAVE CCIS ACM Multimedia WMT@ACL NTCIR IEEE Transactions on Pattern Analysis and Machine Intelligence NIPS ACL ICLR ICLR LREC ACL/IJCNLP (Short Papers) NEWS@IJCNLP LREC ACL LREC LREC ACL ArXiv ICLR NAACL-HLT (1) CoNLL WMT (2) ACL LoResMT@AMTA HLT-NAACL CCL NGT@EMNLP-IJCNLP LREC ICLR
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