The proposed method is language independent because it is purely appearance based , where neither lexical information nor any other statistical language models are required . Moreover , since it does not need word segmentation , it can be applied to Eastern languages where they do not put clear spacing between words . The first half of the paper describes the algorithm to retrieve document image regions which have similar appearance to the given query image . While multiple segmentations are possible even with the same vocabulary , BPE splits words into unique sequences ; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors . So far , the only way to overcome this BPE imperfection , its deterministic nature , was to create another subword segmentation algorithm ( Kudo , 2018 ) . In contrast , we show that BPE itself incorporates the ability to produce multiple segmentations of the same word . There are several approaches for improving neural machine translation for low-resource languages : Monolingual data can be exploited via pretraining or data augmentation ; Parallel corpora on related language pairs can be used via parameter sharing or transfer learning in multilingual models ; Subword segmentation and regularization techniques can be applied to ensure high coverage of the vocabulary . We review these approaches in the context of an asymmetric-resource one-to-many translation task , in which the pair of target languages are related , with one being a very low-resource and the other a higher-resource language . In order to improve performance on crossword questions , the training data is increased to include crossword questions and answers , and this serves to improve results on definitions as well as crossword questions . The final experiments are conducted using sub-word unit segmentation , first on the source side and then later preliminary experimentation is conducted to facilitate character-level output . Initially , an exact reproduction of the baseline results proves unsuccessful . The intra-segment variability of each sound class is modeled by an HMM , and the sound-to-sound transition rules are characterized by a probabilistic intersegment transition matrix . An acoustically-derived lexicon is used to construct word models based upon subword segment models . The proposed segment model was tested on a speaker-trained , isolated word , speech recognition task with a vocabulary of 1109 basic English words . It provides open-source C++ and Python implementations for subword units . While existing subword segmentation tools assume that the input is pre-tokenized into word sequences , SentencePiece can train subword models directly from raw sentences , which allows us to make a purely end-to-end and language independent system . We perform a validation experiment of NMT on English-Japanese machine translation , and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences . For the competition of Chinese word segmentation held in the first CIPS-SIGHNA joint conference . We applied a subwordbased word segmenter using CRFs and extended the segmenter with OOV words recognized by Accessor Variety . Moreover , we proposed several post-processing rules to improve the performance . The basic idea of this method is based on peaks detection and baseline . Inspired by three characteristics , we develop three steps to word and subword segmentation approach : in the word or sub-words segmentation step , a peaks detection function is adopted to model the maximum and minimum peaks . To get the model parameters , a stroke operator is utilized to extract of potential segmentation points ; then a determine baseline process is developed to estimate the parameters depend on the mostly minimum peaks and determine nearest vertexes point to minimum peak on the baseline to confirm the minimum peak as segmentation point or not . The invention discloses a news public opinion monitoring system . The news public opinion monitoring system comprises a news information gathering module , a news data pre-processing module , a news public opinion analyzing module and a news public opinion result display module ; the news data pre-processing module comprises a primary filtering sub-module , a text extracting sub-module , a word segmentation sub-module , a feature phrase filtering sub-module , a text emotion tendency analyzing sub-module , a picture analyzing sub-module and a public opinion popularity acquiring sub-module . A distributed cloud computing mode is used for the data pre-processed by the news data pre-processing module , various news public opinion monitoring algorithm sub-modules are used for acquiring the hot spots of news public opinions , and the hot spots of news public opinions are comprehensively judged , classified and evaluated to effectively and precisely monitor hot topics of the news public opinions . Then a segmentation approach was used to segment the continuous speech into uniquely identifiable and meaningful units . Among the different techniques , the word/sub-word segmentation is simple and produces very good results . This is why this technique was selected for speech segmentation to obtain improved performance . Their model ’s encoder uses a convolutional layer followed by max - pooling to reduce sequence length , a four - layer highway network , and a bidirectional GRU . The parallelism of the convolutional , pooling , and highway layers allows training speed comparable to subword - level models without hard - coded text segmentation . The QRNN encoder – decoder model shares the favorable parallelism and path - length properties exhibited by the ByteNet Kalchbrenner2016 , an architecture for character - level machine translation based on residual convolutions over binary trees . This has continued with the more recently proposed paradigm of neural machine translation , although neural networks do not suffer from character - level modelling and rather suffer from the issues specific to word - level modelling , such as the increased computational complexity from a very large target vocabulary . Therefore , in this paper , we address a question of whether neural machine translation can be done directly on a sequence of characters without any explicit word segmentation . To answer this question , we focus on representing the target side as a character sequence . Although it is generally easy for languages like English , this is not always the case . This word segmentation procedure can be as simple as tokenization followed by some punctuation normalization , but also can be as complicated as morpheme segmentation requiring a separate model to be trained in advance . Furthermore , these segmentation steps are often tuned or designed separately from the ultimate objective of translation quality , potentially contributing to a suboptimal quality . paragraph : Why not Word - Level Translation ? The most pressing issue with word - level processing is that we do not have a perfect word segmentation algorithm for any one language . A perfect segmentation algorithm needs to be able to segment any given sentence into a sequence of lexemes and morphemes . We use beamsearch to approximately find the most likely translation given a source sentence . The beam widths are and respectively for the subword - level and character - level decoders . They were chosen based on the translation quality on the development set . Neural Machine Translation of Rare Words with Subword Units section : Abstract This is based on the intuition that various word classes are translatable via smaller units than words , for instance names ( via character copying or transliteration ) , compounds ( via compositional translation ) , and cognates and loanwords ( via phonological and morphological transformations ) . We discuss the suitability of different word segmentation techniques , including simple character ngram models and a segmentation based on the byte pair encoding compression algorithm , and empirically show that subword models improve over a back - off dictionary baseline for the WMT 15 translation tasks English→German and English→Russian by up to 1.1 and 1.3 BLEU , respectively . section : Introduction We expect that the attention mechanism benefits from our variable - length representation : the network can learn to place attention on different subword units at each step . Recall our introductory example Abwasserbehandlungsanlange , for which a subword segmentation avoids the information bottleneck of a fixed - length representation . Neural machine translation differs from phrasebased methods in that there are strong incentives to minimize the vocabulary size of neural models to increase time and space efficiency , and to allow for translation without back - off models . We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs , each trained in both directions : English Czech , English German , English Romanian and English Russian . Our systems are based on an attentional encoder - decoder , using BPE subword segmentation for open - vocabulary translation with a fixed vocabulary . We experimented with using automatic back - translations of the monolingual News corpus as additional training data , pervasive dropout , and target - bidirectional models . This paper proposes a circuit-sharing approach to improve efficiency for the key digital audio broadcasting ( DAB ) techniques , i.e. , MPEG1-audio decoding and orthogonal frequency division multiplexing ( OFDM ) . Because OFDM 's fast Fourier transform ( FFT ) requires heavy computational power for implementation , a single butterfly processing element ( BPE ) is adopted to reduce the chip area required for FFT . Furthermore , by modifying the BPE logical combinational circuit , both IMDCT ( inverse modified discrete cosine transform ) and FFT functions can be obtained simultaneously from a single VLSI chip . This paper explores the use of Genetic Algorithm ( CA ) as a learning rule for Feed-forward Neural Networks ( F " ) . An extended comparison between CA and Back Propagation Error ( BPE ) are presented and the potential of information induction and reduction from the training set is revised for both methods . The CA was based only in the crossover and mutation genetic operators . Building performance evaluation considers the whole building system from a fabric , service and occupant perspective , as well as its response to the environment and potential integration into smart cities . There are many BPE methods used to collect building data and inform designs . Energy and environment monitoring , simulation , building surveys , post occupancy evaluation , element and whole building testing and their link to future development are discussed . In this paper , we apply NMT for English-Tamil language pair . We propose a novel neural machine translation technique using word-embedding along with Byte-Pair-Encoding ( BPE ) to develop an efficient translation system that overcomes the OOV ( Out Of Vocabulary ) problem for languages which do not have much translations available online . We use the BLEU score for evaluating the system performance . Several migration aspects must be taken into account . ( 1 ) Process models from the old BPE platform have to be converted to the target process definition language on the target BPE platform . ( 2 ) Existing Business Process Management ( BPM ) applications must be integrated via new BPE platform interfaces . This work devises a complex negotiation mechanism that supports negotiation activities among three types of participants in multiple interrelated markets . The complex negotiation mechanism consists of : 1 ) a bargaining-position-estimation ( BPE ) strategy for the multilateral negotiations between consumer and broker agents in a service market and 2 ) a regression-based coordination ( RBC ) strategy for concurrent negotiations between broker and provider agents in multiple resource markets . The negotiation outcomes between broker and provider agents in a resource market can potentially influence the negotiation outcomes between broker and consumer agents in a service market . Empirical results show that agents adopting the BPE strategy can better respond to different market conditions than agents adopting the time-dependent strategy because they do not make excessive ( respectively , inadequate ) amounts of concessions in favorable ( respectively , unfavorable ) markets . In the concurrent negotiations in multiple resource markets , empirical results show that broker agents adopting the RBC strategy achieved significantly higher utilities , higher success rates , and faster negotiation speed than broker agents adopting the utility-oriented and patient coordination strategies . Experimental results show that phone-based BPEs lead to more accurate recognition systems than the character-based counterpart , and further improvement can be obtained with the newly developed one-pass beam search decoder , which efficiently combines both phone-based and character-based BPE systems . For Switchboard , our phone-based BPE system achieves 6.8%/14.4 % word error rate ( WER ) on the Switchboard/CallHome portion of the test set while the ensemble system achieves 6.3%/13.3 % WER . The total number of TR cycles and , hence , the total scan time can be reduced . The resultant signal-to-noise ratio ( SNR ) often varies across the reconstructed image when using the BPE technique , and the image SNR depends on the reconstruction method . This work is comparable to a gradient based version of parallel imaging . As the datasets for this task are rather small compared to MT , we present a novel stopping criterion that prevents overfitting the BPE encoding to the training set . Additionally , we present AST BPE , which is a version of BPE that uses the Abstract Syntax Tree ( AST ) of the SQL statement to guide BPE merges and therefore produce BPE encodings that generalize better . We improved the accuracy of a strong attentive seq2seq baseline on five out of six English text-to-SQL tasks while reducing training time by more than 50 % on four of them due to the shortened targets . Based on the segmentation , we propose a BPE-PCFGs model to generate password guesses . Furthermore , we also utilize the existing common semantic patterns and BPE patterns to construct a new Rich-BPE-PCFGs password generator . Experimental results on large-scale password sets show that our Rich-BPE-PCFGs model can obtain a 2.36%–37.56 % improvement over the original PCFGs model , which is a good complement to existing password guessing algorithms . • We adapt byte pair encoding ( BPE ) [ reference ] , a compression algorithm , to the task of word segmentation . BPE allows for the representation of an open vocabulary through a fixed - size vocabulary of variable - length character sequences , making it a very suitable word segmentation strategy for neural network models . There is some inconsistency between BLEU and CHRF3 , which we attribute to the fact that BLEU has a precision bias , and CHRF3 a recall bias . For English→German , we observe the best BLEU score of 25.3 with C2 - 50k , but the best CHRF3 score of 54.1 with BPE - J90k . For comparison to the ( to our knowledge ) best non - neural MT system on this data set , we report syntaxbased SMT results [ reference ] . We introduce a variant of byte pair encoding for word segmentation , which is capable of encoding open vocabularies with a compact symbol vocabulary of variable - length subword units . We show performance gains over the baseline with both BPE segmentation , and a simple character bigram segmentation . Our analysis shows that not only out - ofvocabulary words , but also rare in - vocabulary words are translated poorly by our baseline NMT system , and that reducing the vocabulary size of subword models can actually improve performance . Their model implements the attention - based sequence to sequence architecture of [ reference ] and uses GRU cells both in the encoder and decoder . We test both word - based and BPE vocabularies ( § 4 ) . Table 1 shows that our fully convolutional sequence to sequence model ( ConvS2S ) outperforms the WMT'16 winning entry for English - Romanian by 1.9 BLEU with a BPE encoding and by 1.3 BLEU with a word factored vocabulary . This instance of our architecture has 20 layes in the encoder and 20 layers in the decoder , both using kernels of width 3 and hidden size 512 throughout . For English Russian , we can not effectively learn BPE on the joint vocabulary because alphabets differ . We thus follow the approach described in , first mapping the Russian text into Latin characters via ISO - 9 transliteration , then learning the BPE operations on the concatenation of the English and latinized Russian training data , then mapping the BPE operations back into Cyrillic alphabet . We apply the Latin BPE operations to the English data ( training data and input ) , and both the Cyrillic and Latin BPE operations to the Russian data . Grammar based compression , where one replaces a long string by a small contextfree grammar that generates the string , is a simple and powerful paradigm that captures many of the popular compression schemes , including the Lempel-Ziv family , Run-Length Encoding , Byte-Pair Encoding , Sequitur and Re-Pair . Subword segmentation is widely used to address the open vocabulary problem in machine translation . The dominant approach to subword segmentation is Byte Pair Encoding ( BPE ) , which keeps the most frequent words intact while splitting the rare ones into multiple tokens . While multiple segmentations are possible even with the same vocabulary , BPE splits words into unique sequences ; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors . In order to compare orthogonal encoding , five byte encoding , Codon encoding(two ) and Profile encoding to find out their virtues and shortcomings , we carefully studied them with artificial neural network . Results indicated that Profile encoding that preserves the redundant evolutionary information got higher prediction performance , and that how to use biologic evolutionary information effectively is very important to improve accuracy of protein secondary structure prediction . Further work needs to be done in incorporating the UCS into historical operating systems , programming languages , existing software programs , and network protocols . In this paper , we review internal and external representations of the UCS , and propose a byte encoding for the UCS . The proposed encoding is a better external code for data storage and transmission in terms of the bit usage and code spaces for character representations . This work focuses on automatically generating regular expressions and proposes a novel genetic algorithm to deal with this problem . Different from the methods which generate regular expressions from character level , we first utilize byte pair encoder ( BPE ) to extract some frequent items , which are then used to construct regular expressions . The fitness function of our genetic algorithm contains multi objectives and is solved based on evolutionary procedure including crossover and mutation operation . Therefore , the existing approaches that use words as units of input often do not work well here . We used the byte pair encoding compression algorithm to build a new vocabulary for the inputs of the classifier . By using this new unit as a feature of the convolutional neural network-based classifier , we improved the accuracy of the dialogue act classification while suppressing the size of vocabulary . This is based on the intuition that various word classes are translatable via smaller units than words , for instance names ( via character copying or transliteration ) , compounds ( via compositional translation ) , and cognates and loanwords ( via phonological and morphological transformations ) . We discuss the suitability of different word segmentation techniques , including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm , and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU , respectively . If any byte changed during transmission , the remaining data can be retrieved safely . In this paper , a try has been made to develop few algorithms for random access text compression based on the byte pair encoding scheme ( Gage , 1997 ) . The BPE algorithm relies on the fact that ASCII character set uses only codes from 0 through 127 . That frees up codes from 128 through 255 for use as pair codes . Pair code is a byte , used to replace the most frequently appearing pair of bytes in the text file . Five algorithms are developed based on this byte pair encoding scheme . These algorithms finds the unused bytes at each level and tries to use those bytes for replacing the most frequently used bytes In this paper , we propose a refined multi-stage multi-task training strategy to improve the performance of online attention-based encoder-decoder ( AED ) models . A three-stage training based on three levels of architectural granularity namely , character encoder , byte pair encoding ( BPE ) based encoder , and attention decoder , is proposed . Also , multi-task learning based on two-levels of linguistic granularity namely , character and BPE , is used . In this paper we report on FBK ’s participation to the English-to-German news translation task of the Second Conference on Machine Translation ( WMT’17 ) . The submitted system is based on Neural Machine Translation using byte-pair encoding segmentation on both source and target languages for open-vocabulary translations . Back-translations of news monolingual data are used for improving the translations fluency on the in-domain data . While , and have shown state of the art results for language modeling and task transfer with character - level mLSTM models , we found that our Transformer model benefits from modeling language through subword units . Using a byte - pair - encoding ( BPE ) of various sized we notice that a 32 , 000 word - piece vocabulary achieves a better bits per character ( BPC ) loss over one epoch of the Amazon Reviews dataset than a small vocabulary . We compute the BPC equivalent over word pieces , following . As noted in Table [ reference ] , a self - attention layer connects all positions with a constant number of sequentially executed operations , whereas a recurrent layer requires sequential operations . In terms of computational complexity , self - attention layers are faster than recurrent layers when the sequence length is smaller than the representation dimensionality , which is most often the case with sentence representations used by state - of - the - art models in machine translations , such as word - piece wu2016google and byte - pair sennrich2015neural representations . To improve computational performance for tasks involving very long sequences , self - attention could be restricted to considering only a neighborhood of size in the input sequence centered around the respective output position . This is based on the intuition that various word classes are translatable via smaller units than words , for instance names ( via character copying or transliteration ) , compounds ( via compositional translation ) , and cognates and loanwords ( via phonological and morphological transformations ) . We discuss the suitability of different word segmentation techniques , including simple character ngram models and a segmentation based on the byte pair encoding compression algorithm , and empirically show that subword models improve over a back - off dictionary baseline for the WMT 15 translation tasks English→German and English→Russian by up to 1.1 and 1.3 BLEU , respectively . section : Introduction We experiment with word - based models using a source vocabulary of 200 K types and a target vocabulary of 80 K types . We also consider a joint source and target byte - pair encoding ( BPE ) with 40 K types [ reference ][ reference ] . WMT'14 English - German . We further investigated which type of information ( as captured by the PoS-filtered skipgrams ) has most impact during classification . The results show that combining unigrams and PoS-filtered skipgrams leads to a significant improvement in classification scores over the unigram baseline . Additional experiments show that the most important phrasal features are bigrams and additional useful phrases can be captured by allowing at most 2 skips in the skipgram approach . Text classification is the task of assigning predefined categories to free-text documents based on their content . Traditional approaches used unigram based models for text classification . Unigram based models such as Bag Of Words(BOW ) models are not considering co-occurrence of set of words in a document level . This paper provides Chinese word segmentation based on language analysis problem in text information filtering system . The improved Chinese word segmentation is made of a bigram segmentation and a segmentation correction , new words recognition and disambiguation through the bigram segmentation , check the accuracy of segmentation results using the segmentation correction from the perspective of syntax . It has been proved by experiments that the segmentation not only strengthen the system 's language analysis ability , but also improve the accuracy of text information filtering system when the improved Chinese word segmentation was applied to the text analysis module . To formalize the notion of dialogue behavior , we manually annotate our data using a tagset of student and tutor dialogue acts relative to the tutoring domain . A unigram analysis of our annotated data shows that student learning correlates both with the tutor 's dialogue acts and with the student 's dialogue acts . A bigram analysis shows that student learning also correlates with joint patterns of tutor and student dialogue acts . Mothers and Matters . By augmenting conventional techniques of topic modeling with unigram analysis and community detection , we establish an automated method that generates a comprehensive and meaningful summary of forum conversations over time that also sheds light on patterns of user behavior . We combine these methods to obtain a multiscale representation of what topics are being discussed , what the users are saying about each topic , how the conversation is evolving over time , and how friendships relate to content . We then make use of some extra contextual information , namely the time gap between two adjacent match events , which is in itself a reasonable indicator of segmentation . By integrating this feature into the multigram segmentation , we considerably enhance segmentation performance . The results show that our approach is an effective way to parse a tennis game from a stream of events with minimal human intervention . Finally , remaining regions with sufficient positively classified SIFT keypoint are retained as likely license plate regions . To train the unigram classifier , a set of SIFT keypoints are obtained from a small set of ground truth images where the license plates are labeled . The training of the SIFT-based unigram classifier is found to be optimal when a CVM is used . Our tool is also capable of handling transliterated words . Unbiased tweets were extracted from Twitter related to this specific campaign , and on comparing with manual tagging we were able to achieve 84.47 % accuracy using unigram machine learning approach . This approach helps the government to implement the social campaigns effectively for the betterment of the society . We use Google Translate Research API1 as our Statistical Machine Translation ( SMT ) system to get the N-best lists consisting of possible Turkish translations for a given English sentence . We evaluate the effect of reordering using three simple methods : unigram count ( UC ) , unigram ratio ( UR ) , and first four characters match ( FFCM ) . We collected 720 sentences in order to give to the SMT system , and then we used 3 different sets of Turkish translations of them to evaluate our work on the N-best lists . In this paper , we introduce a simple unsupervised model for morphological segmentation and study how the knowledge of morph length affect the performance of the segmentation task under the Bayesian framework . The model is based on ( Goldwater et al. , 2006 ) unigram word segmentation model and assumes a simple prior distribution over morph length . We experiment this model on two highly related and agglutinative languages namely Tamil and Telugu , and compare our results with the state of the art Morfessor system . We use a uniform prior distribution over all words in the vocabulary , . One may also use a unigram distribution which has been shown to work better on some tasks pereyra2017regularize . Label smoothing is equivalent to adding the KL divergence between and the model prediction to the negative log likelihood ( TokLS , Equation [ reference ] ) . Cross - bigrams : for every pair of bigrams across the premise and hypothesis which share a POS tag on the second word , an indicator feature over the two bigrams . We report results in Table [ reference ] , along with ablation studies for removing the cross - bigram features ( leaving only the cross - unigram feature ) and for removing all lexicalized features . On our large corpus in particular , there is a substantial jump in accuracy from using lexicalized features , and another from using the very sparse cross - bigram features . n. The increase in sequence length is substantial ; one way to reduce sequence length is to leave a shortlist of the k most frequent word types unsegmented . Only the unigram representation is truly open - vocabulary . However , the unigram representation performed poorly in preliminary experiments , and we report translation results with a bigram representation , which is empirically better , but unable to produce some tokens in the test set with the training set vocabulary .
The proposed method is language independent because it is purely appearance based , where neither lexical information nor any other statistical language models are required . Moreover , since it does not need word segmentation , it can be applied to Eastern languages where they do not put clear spacing between words . The first half of the paper describes the algorithm to retrieve document image regions which have similar appearance to the given query image .
While multiple segmentations are possible even with the same vocabulary , BPE splits words into unique sequences ; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors . So far , the only way to overcome this BPE imperfection , its deterministic nature , was to create another subword segmentation algorithm ( Kudo , 2018 ) . In contrast , we show that BPE itself incorporates the ability to produce multiple segmentations of the same word .
There are several approaches for improving neural machine translation for low-resource languages : Monolingual data can be exploited via pretraining or data augmentation ; Parallel corpora on related language pairs can be used via parameter sharing or transfer learning in multilingual models ; Subword segmentation and regularization techniques can be applied to ensure high coverage of the vocabulary . We review these approaches in the context of an asymmetric-resource one-to-many translation task , in which the pair of target languages are related , with one being a very low-resource and the other a higher-resource language .
In order to improve performance on crossword questions , the training data is increased to include crossword questions and answers , and this serves to improve results on definitions as well as crossword questions . The final experiments are conducted using sub-word unit segmentation , first on the source side and then later preliminary experimentation is conducted to facilitate character-level output . Initially , an exact reproduction of the baseline results proves unsuccessful .
The intra-segment variability of each sound class is modeled by an HMM , and the sound-to-sound transition rules are characterized by a probabilistic intersegment transition matrix . An acoustically-derived lexicon is used to construct word models based upon subword segment models . The proposed segment model was tested on a speaker-trained , isolated word , speech recognition task with a vocabulary of 1109 basic English words .
It provides open-source C++ and Python implementations for subword units . While existing subword segmentation tools assume that the input is pre-tokenized into word sequences , SentencePiece can train subword models directly from raw sentences , which allows us to make a purely end-to-end and language independent system . We perform a validation experiment of NMT on English-Japanese machine translation , and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences .
For the competition of Chinese word segmentation held in the first CIPS-SIGHNA joint conference . We applied a subwordbased word segmenter using CRFs and extended the segmenter with OOV words recognized by Accessor Variety . Moreover , we proposed several post-processing rules to improve the performance .
The basic idea of this method is based on peaks detection and baseline . Inspired by three characteristics , we develop three steps to word and subword segmentation approach : in the word or sub-words segmentation step , a peaks detection function is adopted to model the maximum and minimum peaks . To get the model parameters , a stroke operator is utilized to extract of potential segmentation points ; then a determine baseline process is developed to estimate the parameters depend on the mostly minimum peaks and determine nearest vertexes point to minimum peak on the baseline to confirm the minimum peak as segmentation point or not .
The invention discloses a news public opinion monitoring system . The news public opinion monitoring system comprises a news information gathering module , a news data pre-processing module , a news public opinion analyzing module and a news public opinion result display module ; the news data pre-processing module comprises a primary filtering sub-module , a text extracting sub-module , a word segmentation sub-module , a feature phrase filtering sub-module , a text emotion tendency analyzing sub-module , a picture analyzing sub-module and a public opinion popularity acquiring sub-module . A distributed cloud computing mode is used for the data pre-processed by the news data pre-processing module , various news public opinion monitoring algorithm sub-modules are used for acquiring the hot spots of news public opinions , and the hot spots of news public opinions are comprehensively judged , classified and evaluated to effectively and precisely monitor hot topics of the news public opinions .
Then a segmentation approach was used to segment the continuous speech into uniquely identifiable and meaningful units . Among the different techniques , the word/sub-word segmentation is simple and produces very good results . This is why this technique was selected for speech segmentation to obtain improved performance .
Their model ’s encoder uses a convolutional layer followed by max - pooling to reduce sequence length , a four - layer highway network , and a bidirectional GRU . The parallelism of the convolutional , pooling , and highway layers allows training speed comparable to subword - level models without hard - coded text segmentation . The QRNN encoder – decoder model shares the favorable parallelism and path - length properties exhibited by the ByteNet Kalchbrenner2016 , an architecture for character - level machine translation based on residual convolutions over binary trees .
This has continued with the more recently proposed paradigm of neural machine translation , although neural networks do not suffer from character - level modelling and rather suffer from the issues specific to word - level modelling , such as the increased computational complexity from a very large target vocabulary . Therefore , in this paper , we address a question of whether neural machine translation can be done directly on a sequence of characters without any explicit word segmentation . To answer this question , we focus on representing the target side as a character sequence .
Although it is generally easy for languages like English , this is not always the case . This word segmentation procedure can be as simple as tokenization followed by some punctuation normalization , but also can be as complicated as morpheme segmentation requiring a separate model to be trained in advance . Furthermore , these segmentation steps are often tuned or designed separately from the ultimate objective of translation quality , potentially contributing to a suboptimal quality .
paragraph : Why not Word - Level Translation ? The most pressing issue with word - level processing is that we do not have a perfect word segmentation algorithm for any one language . A perfect segmentation algorithm needs to be able to segment any given sentence into a sequence of lexemes and morphemes .
We use beamsearch to approximately find the most likely translation given a source sentence . The beam widths are and respectively for the subword - level and character - level decoders . They were chosen based on the translation quality on the development set .
Neural Machine Translation of Rare Words with Subword Units section : Abstract
This is based on the intuition that various word classes are translatable via smaller units than words , for instance names ( via character copying or transliteration ) , compounds ( via compositional translation ) , and cognates and loanwords ( via phonological and morphological transformations ) . We discuss the suitability of different word segmentation techniques , including simple character ngram models and a segmentation based on the byte pair encoding compression algorithm , and empirically show that subword models improve over a back - off dictionary baseline for the WMT 15 translation tasks English→German and English→Russian by up to 1.1 and 1.3 BLEU , respectively . section : Introduction
We expect that the attention mechanism benefits from our variable - length representation : the network can learn to place attention on different subword units at each step . Recall our introductory example Abwasserbehandlungsanlange , for which a subword segmentation avoids the information bottleneck of a fixed - length representation . Neural machine translation differs from phrasebased methods in that there are strong incentives to minimize the vocabulary size of neural models to increase time and space efficiency , and to allow for translation without back - off models .
We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs , each trained in both directions : English Czech , English German , English Romanian and English Russian . Our systems are based on an attentional encoder - decoder , using BPE subword segmentation for open - vocabulary translation with a fixed vocabulary . We experimented with using automatic back - translations of the monolingual News corpus as additional training data , pervasive dropout , and target - bidirectional models .
This paper proposes a circuit-sharing approach to improve efficiency for the key digital audio broadcasting ( DAB ) techniques , i.e. , MPEG1-audio decoding and orthogonal frequency division multiplexing ( OFDM ) . Because OFDM 's fast Fourier transform ( FFT ) requires heavy computational power for implementation , a single butterfly processing element ( BPE ) is adopted to reduce the chip area required for FFT . Furthermore , by modifying the BPE logical combinational circuit , both IMDCT ( inverse modified discrete cosine transform ) and FFT functions can be obtained simultaneously from a single VLSI chip .
This paper explores the use of Genetic Algorithm ( CA ) as a learning rule for Feed-forward Neural Networks ( F " ) . An extended comparison between CA and Back Propagation Error ( BPE ) are presented and the potential of information induction and reduction from the training set is revised for both methods . The CA was based only in the crossover and mutation genetic operators .
Building performance evaluation considers the whole building system from a fabric , service and occupant perspective , as well as its response to the environment and potential integration into smart cities . There are many BPE methods used to collect building data and inform designs . Energy and environment monitoring , simulation , building surveys , post occupancy evaluation , element and whole building testing and their link to future development are discussed .
In this paper , we apply NMT for English-Tamil language pair . We propose a novel neural machine translation technique using word-embedding along with Byte-Pair-Encoding ( BPE ) to develop an efficient translation system that overcomes the OOV ( Out Of Vocabulary ) problem for languages which do not have much translations available online . We use the BLEU score for evaluating the system performance .
Several migration aspects must be taken into account . ( 1 ) Process models from the old BPE platform have to be converted to the target process definition language on the target BPE platform . ( 2 ) Existing Business Process Management ( BPM ) applications must be integrated via new BPE platform interfaces .
This work devises a complex negotiation mechanism that supports negotiation activities among three types of participants in multiple interrelated markets . The complex negotiation mechanism consists of : 1 ) a bargaining-position-estimation ( BPE ) strategy for the multilateral negotiations between consumer and broker agents in a service market and 2 ) a regression-based coordination ( RBC ) strategy for concurrent negotiations between broker and provider agents in multiple resource markets . The negotiation outcomes between broker and provider agents in a resource market can potentially influence the negotiation outcomes between broker and consumer agents in a service market . Empirical results show that agents adopting the BPE strategy can better respond to different market conditions than agents adopting the time-dependent strategy because they do not make excessive ( respectively , inadequate ) amounts of concessions in favorable ( respectively , unfavorable ) markets . In the concurrent negotiations in multiple resource markets , empirical results show that broker agents adopting the RBC strategy achieved significantly higher utilities , higher success rates , and faster negotiation speed than broker agents adopting the utility-oriented and patient coordination strategies .
Experimental results show that phone-based BPEs lead to more accurate recognition systems than the character-based counterpart , and further improvement can be obtained with the newly developed one-pass beam search decoder , which efficiently combines both phone-based and character-based BPE systems . For Switchboard , our phone-based BPE system achieves 6.8%/14.4 % word error rate ( WER ) on the Switchboard/CallHome portion of the test set while the ensemble system achieves 6.3%/13.3 % WER .
The total number of TR cycles and , hence , the total scan time can be reduced . The resultant signal-to-noise ratio ( SNR ) often varies across the reconstructed image when using the BPE technique , and the image SNR depends on the reconstruction method . This work is comparable to a gradient based version of parallel imaging .
As the datasets for this task are rather small compared to MT , we present a novel stopping criterion that prevents overfitting the BPE encoding to the training set . Additionally , we present AST BPE , which is a version of BPE that uses the Abstract Syntax Tree ( AST ) of the SQL statement to guide BPE merges and therefore produce BPE encodings that generalize better . We improved the accuracy of a strong attentive seq2seq baseline on five out of six English text-to-SQL tasks while reducing training time by more than 50 % on four of them due to the shortened targets .
Based on the segmentation , we propose a BPE-PCFGs model to generate password guesses . Furthermore , we also utilize the existing common semantic patterns and BPE patterns to construct a new Rich-BPE-PCFGs password generator . Experimental results on large-scale password sets show that our Rich-BPE-PCFGs model can obtain a 2.36%–37.56 % improvement over the original PCFGs model , which is a good complement to existing password guessing algorithms .
• We adapt byte pair encoding ( BPE ) [ reference ] , a compression algorithm , to the task of word segmentation . BPE allows for the representation of an open vocabulary through a fixed - size vocabulary of variable - length character sequences , making it a very suitable word segmentation strategy for neural network models .
There is some inconsistency between BLEU and CHRF3 , which we attribute to the fact that BLEU has a precision bias , and CHRF3 a recall bias . For English→German , we observe the best BLEU score of 25.3 with C2 - 50k , but the best CHRF3 score of 54.1 with BPE - J90k . For comparison to the ( to our knowledge ) best non - neural MT system on this data set , we report syntaxbased SMT results [ reference ] .
We introduce a variant of byte pair encoding for word segmentation , which is capable of encoding open vocabularies with a compact symbol vocabulary of variable - length subword units . We show performance gains over the baseline with both BPE segmentation , and a simple character bigram segmentation . Our analysis shows that not only out - ofvocabulary words , but also rare in - vocabulary words are translated poorly by our baseline NMT system , and that reducing the vocabulary size of subword models can actually improve performance .
Their model implements the attention - based sequence to sequence architecture of [ reference ] and uses GRU cells both in the encoder and decoder . We test both word - based and BPE vocabularies ( § 4 ) . Table 1 shows that our fully convolutional sequence to sequence model ( ConvS2S ) outperforms the WMT'16 winning entry for English - Romanian by 1.9 BLEU with a BPE encoding and by 1.3 BLEU with a word factored vocabulary . This instance of our architecture has 20 layes in the encoder and 20 layers in the decoder , both using kernels of width 3 and hidden size 512 throughout .
For English Russian , we can not effectively learn BPE on the joint vocabulary because alphabets differ . We thus follow the approach described in , first mapping the Russian text into Latin characters via ISO - 9 transliteration , then learning the BPE operations on the concatenation of the English and latinized Russian training data , then mapping the BPE operations back into Cyrillic alphabet . We apply the Latin BPE operations to the English data ( training data and input ) , and both the Cyrillic and Latin BPE operations to the Russian data .
Grammar based compression , where one replaces a long string by a small contextfree grammar that generates the string , is a simple and powerful paradigm that captures many of the popular compression schemes , including the Lempel-Ziv family , Run-Length Encoding , Byte-Pair Encoding , Sequitur and Re-Pair .
Subword segmentation is widely used to address the open vocabulary problem in machine translation . The dominant approach to subword segmentation is Byte Pair Encoding ( BPE ) , which keeps the most frequent words intact while splitting the rare ones into multiple tokens . While multiple segmentations are possible even with the same vocabulary , BPE splits words into unique sequences ; this may prevent a model from better learning the compositionality of words and being robust to segmentation errors .
In order to compare orthogonal encoding , five byte encoding , Codon encoding(two ) and Profile encoding to find out their virtues and shortcomings , we carefully studied them with artificial neural network . Results indicated that Profile encoding that preserves the redundant evolutionary information got higher prediction performance , and that how to use biologic evolutionary information effectively is very important to improve accuracy of protein secondary structure prediction .
Further work needs to be done in incorporating the UCS into historical operating systems , programming languages , existing software programs , and network protocols . In this paper , we review internal and external representations of the UCS , and propose a byte encoding for the UCS . The proposed encoding is a better external code for data storage and transmission in terms of the bit usage and code spaces for character representations .
This work focuses on automatically generating regular expressions and proposes a novel genetic algorithm to deal with this problem . Different from the methods which generate regular expressions from character level , we first utilize byte pair encoder ( BPE ) to extract some frequent items , which are then used to construct regular expressions . The fitness function of our genetic algorithm contains multi objectives and is solved based on evolutionary procedure including crossover and mutation operation .
Therefore , the existing approaches that use words as units of input often do not work well here . We used the byte pair encoding compression algorithm to build a new vocabulary for the inputs of the classifier . By using this new unit as a feature of the convolutional neural network-based classifier , we improved the accuracy of the dialogue act classification while suppressing the size of vocabulary .
This is based on the intuition that various word classes are translatable via smaller units than words , for instance names ( via character copying or transliteration ) , compounds ( via compositional translation ) , and cognates and loanwords ( via phonological and morphological transformations ) . We discuss the suitability of different word segmentation techniques , including simple character n-gram models and a segmentation based on the byte pair encoding compression algorithm , and empirically show that subword models improve over a back-off dictionary baseline for the WMT 15 translation tasks English-German and English-Russian by 1.1 and 1.3 BLEU , respectively .
If any byte changed during transmission , the remaining data can be retrieved safely . In this paper , a try has been made to develop few algorithms for random access text compression based on the byte pair encoding scheme ( Gage , 1997 ) . The BPE algorithm relies on the fact that ASCII character set uses only codes from 0 through 127 . That frees up codes from 128 through 255 for use as pair codes . Pair code is a byte , used to replace the most frequently appearing pair of bytes in the text file . Five algorithms are developed based on this byte pair encoding scheme . These algorithms finds the unused bytes at each level and tries to use those bytes for replacing the most frequently used bytes
In this paper , we propose a refined multi-stage multi-task training strategy to improve the performance of online attention-based encoder-decoder ( AED ) models . A three-stage training based on three levels of architectural granularity namely , character encoder , byte pair encoding ( BPE ) based encoder , and attention decoder , is proposed . Also , multi-task learning based on two-levels of linguistic granularity namely , character and BPE , is used .
In this paper we report on FBK ’s participation to the English-to-German news translation task of the Second Conference on Machine Translation ( WMT’17 ) . The submitted system is based on Neural Machine Translation using byte-pair encoding segmentation on both source and target languages for open-vocabulary translations . Back-translations of news monolingual data are used for improving the translations fluency on the in-domain data .
While , and have shown state of the art results for language modeling and task transfer with character - level mLSTM models , we found that our Transformer model benefits from modeling language through subword units . Using a byte - pair - encoding ( BPE ) of various sized we notice that a 32 , 000 word - piece vocabulary achieves a better bits per character ( BPC ) loss over one epoch of the Amazon Reviews dataset than a small vocabulary . We compute the BPC equivalent over word pieces , following .
As noted in Table [ reference ] , a self - attention layer connects all positions with a constant number of sequentially executed operations , whereas a recurrent layer requires sequential operations . In terms of computational complexity , self - attention layers are faster than recurrent layers when the sequence length is smaller than the representation dimensionality , which is most often the case with sentence representations used by state - of - the - art models in machine translations , such as word - piece wu2016google and byte - pair sennrich2015neural representations . To improve computational performance for tasks involving very long sequences , self - attention could be restricted to considering only a neighborhood of size in the input sequence centered around the respective output position .
This is based on the intuition that various word classes are translatable via smaller units than words , for instance names ( via character copying or transliteration ) , compounds ( via compositional translation ) , and cognates and loanwords ( via phonological and morphological transformations ) . We discuss the suitability of different word segmentation techniques , including simple character ngram models and a segmentation based on the byte pair encoding compression algorithm , and empirically show that subword models improve over a back - off dictionary baseline for the WMT 15 translation tasks English→German and English→Russian by up to 1.1 and 1.3 BLEU , respectively . section : Introduction
We experiment with word - based models using a source vocabulary of 200 K types and a target vocabulary of 80 K types . We also consider a joint source and target byte - pair encoding ( BPE ) with 40 K types [ reference ][ reference ] . WMT'14 English - German .
We further investigated which type of information ( as captured by the PoS-filtered skipgrams ) has most impact during classification . The results show that combining unigrams and PoS-filtered skipgrams leads to a significant improvement in classification scores over the unigram baseline . Additional experiments show that the most important phrasal features are bigrams and additional useful phrases can be captured by allowing at most 2 skips in the skipgram approach .
Text classification is the task of assigning predefined categories to free-text documents based on their content . Traditional approaches used unigram based models for text classification . Unigram based models such as Bag Of Words(BOW ) models are not considering co-occurrence of set of words in a document level .
This paper provides Chinese word segmentation based on language analysis problem in text information filtering system . The improved Chinese word segmentation is made of a bigram segmentation and a segmentation correction , new words recognition and disambiguation through the bigram segmentation , check the accuracy of segmentation results using the segmentation correction from the perspective of syntax . It has been proved by experiments that the segmentation not only strengthen the system 's language analysis ability , but also improve the accuracy of text information filtering system when the improved Chinese word segmentation was applied to the text analysis module .
To formalize the notion of dialogue behavior , we manually annotate our data using a tagset of student and tutor dialogue acts relative to the tutoring domain . A unigram analysis of our annotated data shows that student learning correlates both with the tutor 's dialogue acts and with the student 's dialogue acts . A bigram analysis shows that student learning also correlates with joint patterns of tutor and student dialogue acts .
Mothers and Matters . By augmenting conventional techniques of topic modeling with unigram analysis and community detection , we establish an automated method that generates a comprehensive and meaningful summary of forum conversations over time that also sheds light on patterns of user behavior . We combine these methods to obtain a multiscale representation of what topics are being discussed , what the users are saying about each topic , how the conversation is evolving over time , and how friendships relate to content .
We then make use of some extra contextual information , namely the time gap between two adjacent match events , which is in itself a reasonable indicator of segmentation . By integrating this feature into the multigram segmentation , we considerably enhance segmentation performance . The results show that our approach is an effective way to parse a tennis game from a stream of events with minimal human intervention .
Finally , remaining regions with sufficient positively classified SIFT keypoint are retained as likely license plate regions . To train the unigram classifier , a set of SIFT keypoints are obtained from a small set of ground truth images where the license plates are labeled . The training of the SIFT-based unigram classifier is found to be optimal when a CVM is used .
Our tool is also capable of handling transliterated words . Unbiased tweets were extracted from Twitter related to this specific campaign , and on comparing with manual tagging we were able to achieve 84.47 % accuracy using unigram machine learning approach . This approach helps the government to implement the social campaigns effectively for the betterment of the society .
We use Google Translate Research API1 as our Statistical Machine Translation ( SMT ) system to get the N-best lists consisting of possible Turkish translations for a given English sentence . We evaluate the effect of reordering using three simple methods : unigram count ( UC ) , unigram ratio ( UR ) , and first four characters match ( FFCM ) . We collected 720 sentences in order to give to the SMT system , and then we used 3 different sets of Turkish translations of them to evaluate our work on the N-best lists .
In this paper , we introduce a simple unsupervised model for morphological segmentation and study how the knowledge of morph length affect the performance of the segmentation task under the Bayesian framework . The model is based on ( Goldwater et al. , 2006 ) unigram word segmentation model and assumes a simple prior distribution over morph length . We experiment this model on two highly related and agglutinative languages namely Tamil and Telugu , and compare our results with the state of the art Morfessor system .
We use a uniform prior distribution over all words in the vocabulary , . One may also use a unigram distribution which has been shown to work better on some tasks pereyra2017regularize . Label smoothing is equivalent to adding the KL divergence between and the model prediction to the negative log likelihood ( TokLS , Equation [ reference ] ) .
Cross - bigrams : for every pair of bigrams across the premise and hypothesis which share a POS tag on the second word , an indicator feature over the two bigrams . We report results in Table [ reference ] , along with ablation studies for removing the cross - bigram features ( leaving only the cross - unigram feature ) and for removing all lexicalized features . On our large corpus in particular , there is a substantial jump in accuracy from using lexicalized features , and another from using the very sparse cross - bigram features .
n. The increase in sequence length is substantial ; one way to reduce sequence length is to leave a shortlist of the k most frequent word types unsegmented . Only the unigram representation is truly open - vocabulary . However , the unigram representation performed poorly in preliminary experiments , and we report translation results with a bigram representation , which is empirically better , but unable to produce some tokens in the test set with the training set vocabulary .
Automatic Keyword Extraction from Historical Document Images BPE-Dropout: Simple and Effective Subword Regularization Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation Finding the Answers with Definition Models A segment model based approach to speech recognition SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing High OOV-Recall Chinese Word Segmenter A New Rule to Reconfirm Potential Segmentation Points with Vertexes Points of VDS News public opinion monitoring system Speech recognition front-end for segmenting and clustering continuous Bangla speech Quasi-Recurrent Neural Networks A Character-level Decoder without Explicit Segmentation for Neural Machine Translation Neural Machine Translation of Rare Words with Subword Units Edinburgh Neural Machine Translation Systems for WMT 16 FFT and IMDCT circuit sharing in DAB receiver Genetic Algortithm like Learning Rule for Neural Networks 1993 IEEE Systems, Man and Cybernetics Conference Proceedings Buildings that Perform: Thermal Performance and Comfort Neural Machine Translation for English-Tamil Methodology for migration of long running process instances in a global large scale BPM environment in Credit Suisse's SOA landscape Complex and Concurrent Negotiations for Multiple Interrelated e-Markets An investigation of phone-based subword units for end-to-end speech recognition Bunched phase encoding (BPE): a new fast data acquisition method in MRI. Byte-Pair Encoding for Text-to-SQL Generation Improving Password Guessing Using Byte Pair Encoding Neural Machine Translation of Rare Words with Subword Units Convolutional Sequence to Sequence Learning Edinburgh Neural Machine Translation Systems for WMT 16 Algorithms on Grammar-Compressed Strings BPE-Dropout: Simple and Effective Subword Regularization Research on Encode Influencing Protein Secondary Structure Prediction Distributed multilingual applications of the OSI architecture Revisiting Regex Generation for Modeling Industrial Applications by Incorporating Byte Pair Encoder Dialogue Act Classification in Reference Interview Using Convolutional Neural Network with Byte Pair Encoding Neural Machine Translation of Rare Words with Subword Units New Algorithms For Random Access Text Compression Improved Multi-Stage Training of Online Attention-Based Encoder-Decoder Models FBK's Participation to the English-to-German News Translation Task of WMT 2017 Practical Text Classification With Large Pre-Trained Language Models Attention is All you Need Neural Machine Translation of Rare Words with Subword Units Convolutional Sequence to Sequence Learning Using skipgrams and PoS-based feature selection for patent classification Text Classification by Augmenting Bag of Words (BOW) Representation with Co-occurrence Feature Application of Chinese Word Segmentation Based on Linguistic Environment Analysis in Text Information Filtering System Correlations between dialogue acts and learning in spoken tutoring dialogues An Automated Multiscale Map of Conversations: Mothers and Matters Shallow Parsing of a Tennis Game from Audio Events Detection of license plate characters in natural scene with MSER and SIFT unigram classifier Sentiment analysis on social campaign “Swachh Bharat Abhiyan” using unigram method The feasibility analysis of re-ranking for N-best lists on English-Turkish machine translation The Study of Effect of Length in Morphological Segmentation of Agglutinative Languages Classical Structured Prediction Losses for Sequence to Sequence Learning A large annotated corpus for learning natural language inference Neural Machine Translation of Rare Words with Subword Units
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