Data interlinking is a difficult task particularly in a multilingual environment like the Web . In this paper , we evaluate the suitability of a Machine Translation approach to interlink RDF resources described in English and Chinese languages . We represent resources as text documents , and a similarity between documents is taken for similarity between resources . We describe the structure of the system and the various compaction techniques we developed in order to produce 10 words summaries of news articles . We also present the score we obtained using two different machine translation systems . We present a report on our participation in the English-Indonesian image ad- hoc task of the 2005 Cross-Language Evaluation Forum ( CLEF ) . We chose to translate the Indonesian query set into English using a commercial machine translation tool called Transtool . We show that some improvement in retrieval effectiveness can be obtained using a query expansion technique . Neural machine translation ( NMT ) is an emerging machine translation paradigm that translates texts with an encoder-decoder neural architecture . Very recent studies find that translation quality drops significantly when NMT translates long sentences . The last few years have witnessed a surge in the interest of a new machine translation paradigm : neural machine translation ( NMT ) . Neural machine translation is starting to displace its corpus-based predecessor , statistical machine translation ( SMT ) . In this paper we address the problem of diacritic error detection and restoration — the task of identifying and correcting missing accents in text . In particular , we evaluate the performance of a simple part-of-speech tagger-based technique comparing it to other established methods for error detection/restoration : unigram frequency , decision lists , discriminative classifiers , a machine-translation based method , and grapheme-based approaches . In languages such as Spanish ( the focus here ) , diacritics play a key role in disambiguation and results show that a straightforward modification to an n-gram tagger can be used to achieve good performance in diacritic error identification without resorting to any specialized machinery . We compare a baseline system of exact mention string-matching to ones that include local mention context information as well as information from an existing machine translation system . It turns out that the machine translation-based technique outperforms the baseline , but local entity context similarity does not . This helps to point the way for future crossdocument coreference work in languages with few existing resources for the task . The further application of a mechanism motivated from within monolingual text generation , ' grammatical metaphor ' , then allows candidate appropriate translations to be isolated . The incorporation of these essentially monolingual mechanisms within the machine translation process promises to significantly improve translational capabilities ; examples of this are presented for English and German . We investigate the transformation of Middle Dutch into Modern Dutch by means of machine translation . We demonstrate that by using character overlap the performance of the machine translation process can be improved for this task . SL interpreters are required for people who do not have the knowledge of SL , but interpreters are not readily available . Thus , a machine-based translation system is required to translate the text into SL . In this article , a system is implemented for translating English text into Indian Sign Language ( ISL ) . To respond to this , we designed and evaluated a predictive system based on the logistic regression , and used it to select the top most appropriate machine-based translations . An evaluation of this approach shows retrieval performance is better than using the best machine-based translation . A system called Npae- Rbmt has been developed in this research . Transfer- based machine translation is one instance of rule- based machine-translation approaches and is current ly one of the most widely used methods of machine translation . The idea of transfer-based mac hine translation it is necessary to have an intermediate representation that captures the " mean ing " of the original sentence in order to generate the correct translation . Language is a system of communication that links different multilingual societies . Machine language translation techniques have improved productivity and quality in translation , online communication , online business and trade . It also demonstrates the integral need for innovative technological solutions to the age-old difficulties of the digital divide due to the language barriers . From this experiment , we have found the following . ( 1 ) It is important that a machine translation machine make it visible in a 3D virtual space . A user can easily learn the usage of a machine translation . There is need to fill the gap between two entities which use different languages to communicate which can be done either via human translation or machine translation means . The world has over 7000 living natural languages , thus making it costly if human translation route is taken hence need for machine translation . section : Related Work LSTM [ reference ] showed its early potentials in NLP when a neural machine translation system that leverages LSTM source encoding gave highly competitive results compared to the best SMT models [ reference ] . LSTM encoders have since been explored for other tasks , including syntactic parsing [ reference ] , text classification [ reference ] and machine reading section : Knowledge Distillation for NMT The large sizes of neural machine translation systems make them an ideal candidate for knowledge distillation approaches . In this section we explore three different ways this technique can be applied to NMT . We introduce a non - autoregressive translation model based on the Transformer network vaswani2017attention . We modify the encoder of the original Transformer network by adding a module that predicts fertilities , sequences of numbers that form an important component of many traditional machine translation models brown1993mathematics . These fertilities are supervised during training and provide the decoder at inference time with a globally consistent plan on which to condition its simultaneously computed outputs . However , as the shared task systems were not restricted to the NUCLE training set , all the submissions also used differing amounts of training data from various sources . In addition , the CoNLL systems are mostly combinations of many alternative models : the CAMB system is a hybrid of machine translation , a rule - based system , and a language model re - ranker ; CUUI consists of different classifiers for each individual error type ; and P1 + P2 + S1 + S2 is a combination of four different error correction systems . In contrast , the Bi - LSTM is a single model for detecting all error types , and therefore represents a more scalable data - driven approach . While the added error types tend to be less frequent and more complicated to capture , the added coverage is indeed beneficial for error detection . Combining the patternbased approach with the machine translation system ( Ann + PAT + MT ) gave the best overall performance on all datasets . The two frameworks learn to generate different types of errors , and taking advantage of both leads to substantial improvements in error detection . Since introduced a neural probabilistic language model which uses a neural network to model the conditional probability of a word given a fixed number of the preceding words , neural networks have widely been used in machine translation . However , the role of neural networks has been largely limited to simply providing a single feature to an existing statistical machine translation system or to re - rank a list of candidate translations provided by an existing system . For instance , proposed using a feedforward neural network to compute the score of a pair of source and target phrases and to use the score as an additional feature in the phrase - based statistical machine translation system . e.g. English to French . In a language translation model , the input and output sentences can have different lengths . Although our case is analogous to the NMT , the input and output sequences always have the same length while the input vectors to the encoder and decoder have different dimensions . Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering 's . We tried to fill this gap in the literature with a custom Query Expansion engine trained and tested on open datasets . We automatically generate dialog data for Telugu in the tourist domain , using a knowledge base that provides tourist place , type , tour time , etc . Using this data , we train a sequence to sequence model to learn system responses in the dialog . In order to add the query prediction for information retrieval ( through API calls ) , we train a memory network . Early studies on sentence summarization involve handcrafted rules , syntactic tree pruning and statistical machine translation techniques . Recently , the application of the attentional sequence - to - sequence ( s2s ) framework has attracted growing attention in this area . As we know , sentence summarization inevitably needs to fuse different parts in the source sentence and is abstractive . just presented to define a joint distribution on syntax trees ( ) and words ( ) . This distribution is defined as a sequence model over generator transitions that is parameterized using a continuous space embedding of the algorithm state at each time step ( ) ; i.e. , and where action - specific embeddings and bias vector are parameters in . The representation of the algorithm state at time , , is computed by combining the representation of the generator ’s three data structures : the output buffer ( ) , represented by an embedding , the stack ( ) , represented by an embedding , and the history of actions ( ) taken by the generator , represented by an embedding , where and are parameters . By evaluating on the prediction of clinically relevant outcomes , we show that in a practical setting , unsupervised representation learning can offer clear performance benefits over end-to-end supervised architectures . We experiment with using sequence-to-sequence ( Seq2Seq ) models in two different ways , as an autoencoder and as a forecaster , and show that the best performance is achieved by a forecasting Seq2Seq model with an integrated attention mechanism , proposed here for the first time in the setting of unsupervised learning for medical time series . Sentence simplification is the task of rewriting texts so they are easier to understand . Recent research has applied sequence-to-sequence ( Seq2Seq ) models to this task , focusing largely on training-time improvements via reinforcement learning and memory augmentation . One of the main problems with applying generic Seq2Seq models for simplification is that these models tend to copy directly from the original sentence , resulting in outputs that are relatively long and complex . This paper presents a novel , fully data-driven , and knowledge-grounded neural conversation model aimed at producing more contentful responses without slot filling . We generalize the widely-used Seq2Seq approach by conditioning responses on both conversation history and external " facts " , allowing the model to be versatile and applicable in an open-domain setting . Our approach yields significant improvements over a competitive Seq2Seq baseline . Particularly , Recurrent Neural Networks ( RNNs ) have shown excellent performance on human motion prediction task and other tasks that depend on sequential data , where preserving the order of the sequence items is crucial . The well-known Sequence-to-Sequence ( Seq2Seq ) architectures have been used for sequence learning where two RNNs namely the encoder and the decoder work cooperatively to transform one sequence to another . In the context of neural machine translation , the use of attention decoders yields state-of-the-art results . In current study , Seq2Seq models have been used for eBay product description summarization . We propose a novel Document-Context based Seq2Seq models using RNNs for abstractive and extractive summarizations . Intuitively , this is similar to humans reading the title , abstract or any other contextual information before reading the document . While most existing research proposes hand-crafted and develop-defined reward functions for reinforcement , we devise a novel reward mechanism based on a variant of Beam Search and one-character user-feedback at each step . Experiments show that our model , when trained on a small and shallow Seq2Seq network , successfully promotes the generation of meaningful , diverse and interesting responses , and can be used to train agents with customized personas and conversational styles . Our network introduces more target information to improve diversity , and captures direct semantic information to better constrain the relevance simultaneously . Moreover , the proposed method can be easily applied to any Seq2Seq structure . Extensive experiments on several dialogue corpuses show that our method effectively improves both semantic consistency and diversity of generated responses , and achieves better performance than other competitive methods . Abstractive summarization aims to rewrite a long document to its shorter form , which is usually modeled as a sequence-to-sequence ( Seq2Seq ) learning problem . Seq2Seq Transformers are powerful models for this problem . Unfortunately , training large Seq2Seq Transformers on limited supervised summarization data is challenging . This type of model is shown to work effectively with PSK data and also has a number of useful properties that are not present in other machine learning algorithms . A basic Seq2Seq implementation for BPSK and QPSK demodulation is presented in this paper , and learned properties such as Automatic Modulation Classification ( AMC ) , and ability to adapt to different length input sequences , are demonstrated . This is an exciting new avenue of research that provides considerable potential for application in next generation 5 G networks . We propose to view the hidden states of the encoder and the decoder as spatio-temporal snapshots of network dynamics and to apply proper orthogonal decomposition to their concatenation to compute a low-dimensional embedding for hidden state dynamics . Projection of the decoder states onto such interpretable embedding space shows that Seq2Seq training to predict sequences using gradient-descent back propagation effectively performs dimension reduction consisting of only a small percentage of dimensions of the network 's hidden units . Furthermore , sequences are being clustered into well separable clusters in the low dimensional space each of which corresponds to a different type of dynamics . Specifically , we propose a Generative Adversarial Net based prediction framework to address the blurry prediction issue by introducing the adversarial training loss . To predict the traffic conditions in multiple future time intervals simultaneously , we design a sequence to sequence ( Seq2Seq ) based encoder-decoder model as the generator of GCGAN . To fully capture the spatial correlations among the road segments of a transportation network , we propose to apply a graph convolution network ( GCN ) in both generator and discriminator of GCGAN for feature learning .
Data interlinking is a difficult task particularly in a multilingual environment like the Web . In this paper , we evaluate the suitability of a Machine Translation approach to interlink RDF resources described in English and Chinese languages . We represent resources as text documents , and a similarity between documents is taken for similarity between resources .
We describe the structure of the system and the various compaction techniques we developed in order to produce 10 words summaries of news articles . We also present the score we obtained using two different machine translation systems .
We present a report on our participation in the English-Indonesian image ad- hoc task of the 2005 Cross-Language Evaluation Forum ( CLEF ) . We chose to translate the Indonesian query set into English using a commercial machine translation tool called Transtool . We show that some improvement in retrieval effectiveness can be obtained using a query expansion technique .
Neural machine translation ( NMT ) is an emerging machine translation paradigm that translates texts with an encoder-decoder neural architecture . Very recent studies find that translation quality drops significantly when NMT translates long sentences .
The last few years have witnessed a surge in the interest of a new machine translation paradigm : neural machine translation ( NMT ) . Neural machine translation is starting to displace its corpus-based predecessor , statistical machine translation ( SMT ) .
In this paper we address the problem of diacritic error detection and restoration — the task of identifying and correcting missing accents in text . In particular , we evaluate the performance of a simple part-of-speech tagger-based technique comparing it to other established methods for error detection/restoration : unigram frequency , decision lists , discriminative classifiers , a machine-translation based method , and grapheme-based approaches . In languages such as Spanish ( the focus here ) , diacritics play a key role in disambiguation and results show that a straightforward modification to an n-gram tagger can be used to achieve good performance in diacritic error identification without resorting to any specialized machinery .
We compare a baseline system of exact mention string-matching to ones that include local mention context information as well as information from an existing machine translation system . It turns out that the machine translation-based technique outperforms the baseline , but local entity context similarity does not . This helps to point the way for future crossdocument coreference work in languages with few existing resources for the task .
The further application of a mechanism motivated from within monolingual text generation , ' grammatical metaphor ' , then allows candidate appropriate translations to be isolated . The incorporation of these essentially monolingual mechanisms within the machine translation process promises to significantly improve translational capabilities ; examples of this are presented for English and German .
We investigate the transformation of Middle Dutch into Modern Dutch by means of machine translation . We demonstrate that by using character overlap the performance of the machine translation process can be improved for this task .
SL interpreters are required for people who do not have the knowledge of SL , but interpreters are not readily available . Thus , a machine-based translation system is required to translate the text into SL . In this article , a system is implemented for translating English text into Indian Sign Language ( ISL ) .
To respond to this , we designed and evaluated a predictive system based on the logistic regression , and used it to select the top most appropriate machine-based translations . An evaluation of this approach shows retrieval performance is better than using the best machine-based translation .
A system called Npae- Rbmt has been developed in this research . Transfer- based machine translation is one instance of rule- based machine-translation approaches and is current ly one of the most widely used methods of machine translation . The idea of transfer-based mac hine translation it is necessary to have an intermediate representation that captures the " mean ing " of the original sentence in order to generate the correct translation .
Language is a system of communication that links different multilingual societies . Machine language translation techniques have improved productivity and quality in translation , online communication , online business and trade . It also demonstrates the integral need for innovative technological solutions to the age-old difficulties of the digital divide due to the language barriers .
From this experiment , we have found the following . ( 1 ) It is important that a machine translation machine make it visible in a 3D virtual space . A user can easily learn the usage of a machine translation .
There is need to fill the gap between two entities which use different languages to communicate which can be done either via human translation or machine translation means . The world has over 7000 living natural languages , thus making it costly if human translation route is taken hence need for machine translation .
section : Related Work LSTM [ reference ] showed its early potentials in NLP when a neural machine translation system that leverages LSTM source encoding gave highly competitive results compared to the best SMT models [ reference ] . LSTM encoders have since been explored for other tasks , including syntactic parsing [ reference ] , text classification [ reference ] and machine reading
section : Knowledge Distillation for NMT The large sizes of neural machine translation systems make them an ideal candidate for knowledge distillation approaches . In this section we explore three different ways this technique can be applied to NMT .
We introduce a non - autoregressive translation model based on the Transformer network vaswani2017attention . We modify the encoder of the original Transformer network by adding a module that predicts fertilities , sequences of numbers that form an important component of many traditional machine translation models brown1993mathematics . These fertilities are supervised during training and provide the decoder at inference time with a globally consistent plan on which to condition its simultaneously computed outputs .
However , as the shared task systems were not restricted to the NUCLE training set , all the submissions also used differing amounts of training data from various sources . In addition , the CoNLL systems are mostly combinations of many alternative models : the CAMB system is a hybrid of machine translation , a rule - based system , and a language model re - ranker ; CUUI consists of different classifiers for each individual error type ; and P1 + P2 + S1 + S2 is a combination of four different error correction systems . In contrast , the Bi - LSTM is a single model for detecting all error types , and therefore represents a more scalable data - driven approach .
While the added error types tend to be less frequent and more complicated to capture , the added coverage is indeed beneficial for error detection . Combining the patternbased approach with the machine translation system ( Ann + PAT + MT ) gave the best overall performance on all datasets . The two frameworks learn to generate different types of errors , and taking advantage of both leads to substantial improvements in error detection .
Since introduced a neural probabilistic language model which uses a neural network to model the conditional probability of a word given a fixed number of the preceding words , neural networks have widely been used in machine translation . However , the role of neural networks has been largely limited to simply providing a single feature to an existing statistical machine translation system or to re - rank a list of candidate translations provided by an existing system . For instance , proposed using a feedforward neural network to compute the score of a pair of source and target phrases and to use the score as an additional feature in the phrase - based statistical machine translation system .
e.g. English to French . In a language translation model , the input and output sentences can have different lengths . Although our case is analogous to the NMT , the input and output sequences always have the same length while the input vectors to the encoder and decoder have different dimensions .
Using sequence to sequence algorithms for query expansion has not been explored yet in Information Retrieval literature nor in Question-Answering 's . We tried to fill this gap in the literature with a custom Query Expansion engine trained and tested on open datasets .
We automatically generate dialog data for Telugu in the tourist domain , using a knowledge base that provides tourist place , type , tour time , etc . Using this data , we train a sequence to sequence model to learn system responses in the dialog . In order to add the query prediction for information retrieval ( through API calls ) , we train a memory network .
Early studies on sentence summarization involve handcrafted rules , syntactic tree pruning and statistical machine translation techniques . Recently , the application of the attentional sequence - to - sequence ( s2s ) framework has attracted growing attention in this area . As we know , sentence summarization inevitably needs to fuse different parts in the source sentence and is abstractive .
just presented to define a joint distribution on syntax trees ( ) and words ( ) . This distribution is defined as a sequence model over generator transitions that is parameterized using a continuous space embedding of the algorithm state at each time step ( ) ; i.e. , and where action - specific embeddings and bias vector are parameters in . The representation of the algorithm state at time , , is computed by combining the representation of the generator ’s three data structures : the output buffer ( ) , represented by an embedding , the stack ( ) , represented by an embedding , and the history of actions ( ) taken by the generator , represented by an embedding , where and are parameters .
By evaluating on the prediction of clinically relevant outcomes , we show that in a practical setting , unsupervised representation learning can offer clear performance benefits over end-to-end supervised architectures . We experiment with using sequence-to-sequence ( Seq2Seq ) models in two different ways , as an autoencoder and as a forecaster , and show that the best performance is achieved by a forecasting Seq2Seq model with an integrated attention mechanism , proposed here for the first time in the setting of unsupervised learning for medical time series .
Sentence simplification is the task of rewriting texts so they are easier to understand . Recent research has applied sequence-to-sequence ( Seq2Seq ) models to this task , focusing largely on training-time improvements via reinforcement learning and memory augmentation . One of the main problems with applying generic Seq2Seq models for simplification is that these models tend to copy directly from the original sentence , resulting in outputs that are relatively long and complex .
This paper presents a novel , fully data-driven , and knowledge-grounded neural conversation model aimed at producing more contentful responses without slot filling . We generalize the widely-used Seq2Seq approach by conditioning responses on both conversation history and external " facts " , allowing the model to be versatile and applicable in an open-domain setting . Our approach yields significant improvements over a competitive Seq2Seq baseline .
Particularly , Recurrent Neural Networks ( RNNs ) have shown excellent performance on human motion prediction task and other tasks that depend on sequential data , where preserving the order of the sequence items is crucial . The well-known Sequence-to-Sequence ( Seq2Seq ) architectures have been used for sequence learning where two RNNs namely the encoder and the decoder work cooperatively to transform one sequence to another . In the context of neural machine translation , the use of attention decoders yields state-of-the-art results .
In current study , Seq2Seq models have been used for eBay product description summarization . We propose a novel Document-Context based Seq2Seq models using RNNs for abstractive and extractive summarizations . Intuitively , this is similar to humans reading the title , abstract or any other contextual information before reading the document .
While most existing research proposes hand-crafted and develop-defined reward functions for reinforcement , we devise a novel reward mechanism based on a variant of Beam Search and one-character user-feedback at each step . Experiments show that our model , when trained on a small and shallow Seq2Seq network , successfully promotes the generation of meaningful , diverse and interesting responses , and can be used to train agents with customized personas and conversational styles .
Our network introduces more target information to improve diversity , and captures direct semantic information to better constrain the relevance simultaneously . Moreover , the proposed method can be easily applied to any Seq2Seq structure . Extensive experiments on several dialogue corpuses show that our method effectively improves both semantic consistency and diversity of generated responses , and achieves better performance than other competitive methods .
Abstractive summarization aims to rewrite a long document to its shorter form , which is usually modeled as a sequence-to-sequence ( Seq2Seq ) learning problem . Seq2Seq Transformers are powerful models for this problem . Unfortunately , training large Seq2Seq Transformers on limited supervised summarization data is challenging .
This type of model is shown to work effectively with PSK data and also has a number of useful properties that are not present in other machine learning algorithms . A basic Seq2Seq implementation for BPSK and QPSK demodulation is presented in this paper , and learned properties such as Automatic Modulation Classification ( AMC ) , and ability to adapt to different length input sequences , are demonstrated . This is an exciting new avenue of research that provides considerable potential for application in next generation 5 G networks .
We propose to view the hidden states of the encoder and the decoder as spatio-temporal snapshots of network dynamics and to apply proper orthogonal decomposition to their concatenation to compute a low-dimensional embedding for hidden state dynamics . Projection of the decoder states onto such interpretable embedding space shows that Seq2Seq training to predict sequences using gradient-descent back propagation effectively performs dimension reduction consisting of only a small percentage of dimensions of the network 's hidden units . Furthermore , sequences are being clustered into well separable clusters in the low dimensional space each of which corresponds to a different type of dynamics .
Specifically , we propose a Generative Adversarial Net based prediction framework to address the blurry prediction issue by introducing the adversarial training loss . To predict the traffic conditions in multiple future time intervals simultaneously , we design a sequence to sequence ( Seq2Seq ) based encoder-decoder model as the generator of GCGAN . To fully capture the spatial correlations among the road segments of a transportation network , we propose to apply a graph convolution network ( GCN ) in both generator and discriminator of GCGAN for feature learning .
Interlinking English and Chinese RDF Data Sets Using Machine Translation Lakhas, an Arabic summarization system University of Indonesia Participation at WEBIR-CLEF 2005 Automatic Long Sentence Segmentation for Neural Machine Translation Making sense of neural machine translation Spanish Diacritic Error Detection and Restoration - A Survey Arabic Cross-Document Coreference Resolution Finding Translation Equivalents: An Application Of Grammatical Metaphor Using character overlap to improve language transformation Sign Language Generation System Based on Indian Sign Language Grammar Recherche bilingue et multilingue d’information. Vers une sélection des bonnes traductions Machine Translation of Noun Phrases from Arabic to English Using Transfer-Based Approach Automated Language Translation: Opportunities and Impact on the Society Availability of Multilingual Chat Communication in 3D Online Virtual Space A Review on Machine Translation Approaches Sentence-State LSTM for Text Representation Sequence-Level Knowledge Distillation Non-Autoregressive Neural Machine Translation Compositional Sequence Labeling Models for Error Detection in Learner Writing Artificial Error Generation with Machine Translation and Syntactic Patterns Neural Machine Translation by Jointly Learning to Align and Translate Exploiting Temporal Information for 3D Human Pose Estimation Sequence to Sequence Learning for Query Expansion End to End Dialog System for Telugu Faithful to the Original: Fact Aware Neural Abstractive Summarization Recurrent Neural Network Grammars Improving Clinical Predictions through Unsupervised Time Series Representation Learning Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification A Knowledge-Grounded Neural Conversation Model Attention Mechanism for Human Motion Prediction Abstractive and Extractive Text Summarization using Document Context Vector and Recurrent Neural Networks Online Sequence-to-Sequence Reinforcement Learning for Open-Domain Conversational Agents HSCJN: A Holistic Semantic Constraint Joint Network for Diverse Response Generation STEP: Sequence-to-Sequence Transformer Pre-training for Document Summarization Using Sequence to Sequence Learning for Digital BPSK and QPSK Demodulation Dimension Reduction Approach for Interpretability of Sequence to Sequence Recurrent Neural Networks GCGAN: Generative Adversarial Nets with Graph CNN for Network-Scale Traffic Prediction
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KNOW@LOD NLPCC/ICCPOL LTC ACL/IJCNLP (Short Papers) COLING LaTeCH@ACL Culture and Computing ACL EMNLP ICLR ACL BEA@EMNLP ICLR ECCV ICON AAAI HLT-NAACL NAACL-HLT (1) AAAI 5G World Forum IJCNN
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