Results on three public datasets show that SHTM performs much better than LSTM on the measures of mean precision and BLEU score . In addition , we utilize SHTM model to do question answering in the fashion of text generation and verifying its superiority . question asking is a way for the communication of tacher and students in English reading teaching . Well designed questions take the students to a wonderful world , promoting the students to think actively , cultivating their ability to solve problems . The study confirms that students do acquire the skill of question-asking during PBL tutorials . However , to maximize the benefits of question-asking for epistemological and ontological development , more innovative learning activities must be designed and realised in tutorials . Word similarity computing is widely used in many fields , such as question answer , text clustering and so on . This paper synthesizes previous research achievement and proposes an improved word similarity computing method based on Hownet . Information Extraction techniques help identifying certain kinds of information , but their capabilities are quite domain dependent and limited to entities with predefined patterns . Neither of these techniques is sufficient to address the Question Answering problem , but we have explored a way of combining them to build a complete question answering system . The approach The meaning of a document might be expressed in terms of entities and relations between them . From that viewpoint , finding an answer consists not only in finding relevant documents but also in extracting relevant parts . This leads us to express the question-answering problem in terms of an information retrieval problem that can be solved using natural language processing ( NLP ) approaches . In my talk , I will focus on defining what a " good " answer is , and how a system can find it . Further , since conversations have a natural temporal nature , we adopt the temporal nature through recurrent network . Memory networks has been successful in several NLP areas , including question answering , machine translation , speech recognition , and so on . Thus , hazarika - EtAl:2018:N18 - 1 hazarika - EtAl:2018:N18 - 1 used memory networks for emotion recognition in dyadic conversations , where two distinct memory networks enabled inter - speaker interaction , yielding state - of - the - art performance . Our word vectors are learned functions of the internal states of a deep bidirectional language model ( biLM ) , which is pre - trained on a large text corpus . We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems , including question answering , textual entailment and sentiment analysis . We also present an analysis showing that exposing the deep internals of the pre - trained network is crucial , allowing downstream models to mix different types of semi - supervision signals . FastHybrid is a light-weight model that requires little tuning and adaptation across different domains . It combines a fast deep model ( which will be introduced in the method section ) with an initial information retrieval model to effectively and efficiently handle answer selection . We introduce a new efficient attention mechanism in the hybrid model and demonstrate its effectiveness on several QA datasets . An independent model can not comprehend the syntactic and semantic features of questions and answers with a small training dataset . To fill this gap , language models can be employed in implementing the answer selection part . This action enables the model to have a better understanding of the language in order to understand questions and answers better than previous works . Using a more robust classifier also enhances the effect of the language model on the answer selection component . The results demonstrate that language comprehension is an essential requirement in natural language processing tasks such as answer-selection . This paper describes the utility of semantic resources such as the Web , WordNet and gazetteers in the answer selection process for a question-answering system . In contrast with previous work using individual semantic resources to support answer selection , our work combines multiple resources to boost the confidence scores assigned to correct answers and evaluates different combination strategies based on unweighted sums , weighted linear combinations , and logistic regression . Multi-Modal Similarity Metric Learning based Deep Convolutional Network for Answer Selection . Recent works using artificial neural networks based on distributed word representation greatly boost performance on various natural language processing tasks , especially the answer selection problem . Nevertheless , most of the previous works used deep learning methods ( like LSTM-RNN , CNN , etc . ) only to capture semantic representation of each sentence separately , without considering the interdependence between each other . In this paper , we propose a novel end-to-end learning framework which constitutes deep convolutional neural network based on multi-modal similarity metric learning ( M$^2$S-Net ) on pairwise tokens . The proposed model demonstrates its performance by surpassing previous state-of-the-art systems on the answer selection benchmark , i.e. , TREC-QA dataset , in both MAP and MRR metrics . In this paper , the answer selection problem in community question answering ( CQA ) is regarded as an answer sequence labeling task , and a novel approach is proposed based on the recurrent architecture for this problem . Our approach applies convolution neural networks ( CNNs ) to learning the joint representation of question-answer pair firstly , and then uses the joint representation as input of the long short-term memory ( LSTM ) to learn the answer sequence of a question for labeling the matching quality of each answer . In Community-based Question Answering system(CQA ) , Answer Selection(AS ) is a critical task , which focuses on finding a suitable answer within a list of candidate answers . For neural network models , the key issue is how to model the representations of QA text pairs and calculate the interactions between them . Extensive experiments on a real-world dataset have well-validated our model . Meanwhile , we have successfully applied our proposed model to factoid question answering systems for answer candidate selection , which further demonstrates the applicability of the TRANSFER model . A multilevel method is fulfilled to retrieve a candidate Bi-sentence . As to answer selecting , we proposed a voting method . We figure out the performance of each module , and our study shows that 65.54 % information has lost in document retrieval and Bi-sentence retrieval . Our research focuses dialogue-based MRC , where the passages are multi-turn dialogues . It suffers from two challenges , the answer selection decision is made without support of latently helpful commonsense , and the multi-turn context may hide considerable irrelevant information . This work thus makes the first attempt to tackle those two challenges by extracting substantially important turns and utilizing external knowledge to enhance the representation of context . Attention mechanism can then be applied to both structure and node function . We implemented and evaluated a binary - tree model of NTI , showing the model achieved the state - of - the - art performance on three different NLP tasks : natural language inference , answer sentence selection , and sentence classification , outperforming state - of - the - art recurrent and recursive neural networks . section : Introduction section : Neural Answer Selection Model Answer sentence selection is a question answering paradigm where a model must identify the correct sentences answering a factual question from a set of candidate sentences . Assume a question is associated with a set of answer sentences , together with their judgements , where if the answer is correct and otherwise . This paper introduced a deep neural variational inference framework for generative models of text . We experimented on two diverse tasks , document modelling and question answer selection tasks to demonstrate the effectiveness of this framework , where in both cases our models achieve state of the art performance . Apart from the promising results , our model also has the advantages of ( 1 ) simple , expressive , and efficient when training with the SGVB algorithm ; ( 2 ) suitable for both unsupervised and supervised learning tasks ; and ( 3 ) capable of generalising to incorporate any type of neural network . By combining additional features , could our model outperform state - of - the - art models for question answering ? To address these research questions , we analyze the existing current deep learning architectures for answer ranking and make the following two key observations : Architectures not specifically designed for question / answer matching : Some methods employ CNNs for question / answer matching . However , CNNs are originally designed for computer vision ( CV ) , which uses position - shared weights with local perceptive filters , to learn spatial regularities in many CV tasks . With an additional simple feature like QL , our model is significantly better than the previous state - of - the - art methods including deep learning methods . Answer Ranking in CQA . There is also previous research on ranking answers from community question answering ( CQA ) sites . Our model , the Gated - Attention ( GA ) Reader , integrates a multi - hop architecture with a novel attention mechanism , which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader . This enables the reader to build query - specific representations of tokens in the document for accurate answer selection . The GA Reader obtains state - of - the - art results on three benchmarks for this task – the CNN & Daily Mail news stories and the Who “ question”s created by replacing the answer in a sentence from the text with blank . For the answer selection task this paper focuses on , several datasets exist , e.g. TREC - QA for factoid answer extraction from multiple given passages , bAbI designed for inference purpose , and the SQuAD dataset used in this paper . To the best of our knowledge , the SQuAD dataset is the only one for both factoid and non - factoid answer extraction with a question distribution more close to real - world applications . For instance , gated attention model employs a multi - layer architecture , where each layer encodes the same document , but the attention is updated from layer to layer . EpiReader adopted a joint training model for answer extractor and reasoner , where the extractor proposes top candidates , and the reasoner weighs each candidate by examining entailment relationship between question - answer representation and the document . An iterative alternating attention mechanism and gating strategies were proposed in to optimize the attention through several hops . section : 21 section : APPENDIX VIII : ANSWER DISTRIBUTION The top 250 answers in our real images dataset along with their counts and percentage counts are given below . Recent years have seen rapid growth in the MRC community . With the release of various datasets , the MRC task has evolved from the early cloze - style test to answer extraction from a single passage and to the latest more complex question answering on web data . Great efforts have also been made to develop models for these MRC tasks , especially for the answer extraction on single passage . From Table [ reference ] , we can see that the answer verification makes a great contribution to the overall improvement , which confirms our hypothesis that cross - passage answer verification is useful for the multi - passage MRC . For the ablation of the content model , we analyze that it will not only affect the content score itself , but also violate the verification model since the content probabilities are necessary for the answer representation , which will be further analyzed in Section [ reference ] . Another discovery is that jointly training the three models can provide great benefits , which shows that the three tasks are actually closely related and can boost each other with shared representations at bottom layers . Both benchmarks are organized around the task of answer sentence selection , where a system must identify the sentence containing the correct answer in a collection of documents , but need not return the actual answer as a KB - based system would do . Unfortunately , these datasets are very small ( hundreds of examples ) and , because of their answer selection setting , do not offer the option to directly compare answering from a KB against answering from pure text . Using similar resources as the dialog dataset of , our new benchmark WikiMovies addresses both deficiencies by providing a substantial corpus of question - answer pairs that can be answered by either using a KB or a corresponding set of documents . Open domain question answering is slowly broadening its horizons , expanding from simple factoid questions to encompass broader and more complex queries . Recent areas of interest include scenario-based question answering , incorporating existing domain knowledge during question analysis , various types of inference , and intelligently processing subquestions . Starting from these observations , this paper addresses the problem of enhancing interactions between non-expert users and data available on the Web . In particular , we present QAKiS , a system for open domain Question Answering over linked data , that addresses the problem of question interpretation as a relation-based match , where fragments of the question are matched to binary relations of the triple store , using relational textual patterns automatically collected . In the current version , the relational patterns are automatically extracted from Wikipedia , while DBpedia is the data set to be queried using a natural language interface . Applications in business intelligence , health care , customer support , enterprise knowledge management , social computing , science and government would all benefit from such technology . The Project Watson is addressing a grand challenge in Computer Science aimed at illustrating how the integration and advancement of Natural Language Processing ( NLP ) , Information Retrieval ( IR ) , Machine Learning ( ML ) , massively parallel computation and Knowledge Representation and Reasoning ( KR&R ) can advance open-domain automatic Question Answering to a point where it clearly and consistently rivals the best human performance . An exciting proof-point in this challenge is to develop a computer system that can successfully compete against top human players at the well-known Jeopardy ! Automatic open-domain Question Answering has been a long standing research challenge in the AI community . IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson . We introduce shapaqa , a shallow parsing approach to online , open-domain question answering on the WorldWideWeb . Given a form-based natural language question as input , the system uses a memory-based shallow parser to analyze web pages retrieved using normal keyword search on a search engine . Question answering , which is the retrieving of answers to natural language questions from a document collection , tries to remove the onus on the end-user by providing direct access to relevant information . This thesis is concerned with open-domain complex question answering . Unlike simple questions , complex questions can not be answered easily as they often require inferencing and synthesizing information from multiple documents . In this paper , we explore how the state of the art in answer selection has improved recently , comparing two of the best proposed models for tackling the problem : the Crossattentive Convolutional Network and the BERT model . The experiments are carried out on two datasets , WikiQA and SelQA , both created for and used in open-domain question answering challenges . We also report on cross domain experiments with the two datasets . Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup . In order to study the feasibility of neural models specialized for retrieval in a semantically meaningful way , we suggest the use of the Stanford Question Answering Dataset ( SQuAD ) in an open-domain question answering context , where the first task is to find paragraphs useful for answering a given question . First , we compare the quality of various text-embedding methods on the performance of retrieval and give an extensive empirical comparison on the performance of various non-augmented base embedding with , and without IDF weighting . Answer selection is a crucial subtask of the open domain question answering problem . In this paper , we introduce the Bi-directional Gated Memory Network ( BGMN ) to model the interactions between question and answer . We describe a robust accurate domain-independent approach to statistical parsing incorporated into the new release of the ANLT toolkit , and publicly available as a research tool . The system has been used to parse many well known corpora in order to produce data for lexical acquisition efforts ; it has also been used as a component in an open-domain question answering project . The performance of the system is competitive with that of statistical parsers using highly lexicalised parse selection models . In this paper , we propose a new method to generate multi-resolution word embeddings that represent documents at multiple resolutions in terms of context-scopes . In order to investigate its performance , we use the Stanford Question Answering Dataset ( SQuAD ) and the Question Answering by Search And Reading ( QUASAR ) in an open-domain question-answering setting , where the first task is to find documents useful for answering a given question . To this end , we first compare the quality of various text-embedding methods for retrieval performance and give an extensive empirical comparison with the performance of various non-augmented base embeddings with and without multi-resolution representation . Teaching machines how to automatically answer questions asked in natural language on any topic or in any domain has always been a long standing goal in Artificial Intelligence . With the rise of large scale structured knowledge bases ( KBs ) , this problem , known as open - domain question answering ( or open QA ) , boils down to being able to query efficiently such databases with natural language . These KBs , such as Freebase [ reference ] encompass huge ever growing amounts of information and ease open QA by organizing a great variety of answers in a structured format . Syllables are concatenated to generate a synthesized Malayalam speech . Machine translation of English to Malayalam text is tested and achieved 73 percentage accuracy . For the TTS system , accuracy is verified by checking the naturalness and intelligibility . Normalization process calculates equal number of words in English-Hindi sentences to find exact alignment of each word . Second strategy is mapping English-Hindi sentence using parallel English-Hindi word dictionary . Parallel English-Hindi dictionary contains normalized English-Hindi word , which are more able to align than previous other approach of alignment . First is normalization of tagged English sentences and Hindi sentences . Second is mapping English-Hindi sentence using parallel English-Hindi word dictionary . Hence proposed system is desirable to encourage English and Hindi parallel sentences . Code mixing and switching is natural in bilingual and multilingual settings in general and bilingual Hindi English speech community in particular . This book belies the fact that code-mixing and code-switching is not arbitrary , in fact it is natural and patterned . This study aimed to examine the role played by the depth of vocabulary knowledge in the reading comprehension process of Korean EFL learners within the framework of the Simple View of Reading ( SVR ) ( Gough & Tunmer , 1986 ) . In addition to the two major SVR components , decoding and linguistic comprehension of English words , vocabulary depth , was added to the model to see whether it exerted any additional influence on the reading comprehension of Korean high school EFL learners . A total of 207 11th grade high school students in South Korea participated in this study by taking vocabulary breadth , vocabulary depth ( analogy , antonym , and synonym ) , listening comprehension , decoding , and reading comprehension tests . This paper reports on a research project to design a system for automatically interpreting English specifications of digital systems in terms of design representation formalisms currently employed in CAD systems . The necessary processes involve the machine analysis of English and the synthesis of models from the specifications . The approach being investigated is interactive and consists of syntactic scanning , semantic analysis , interpretation generation , and model integration . Abstract This article examines the status of constructed controlled terminologies from the perspective of the coverage of terms/concepts . To facilitate controlled authoring of Japanese texts of the municipal domain and promote machine translatability into English , we constructed terminologies in the following way : ( 1 ) Japanese-English term pairs are extracted from aligned texts ; ( 2 ) term variations are controlled by defining preferred and proscribed terms for both languages . To assess the coverage of the constructed terminologies , we propose a quantitative extrapolation method that estimates the potential vocabulary size . This challenge test set was created by searching for ( particularly ) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity . Our dataset is the first for multimodal English-Hindi machine translation , freely available for non-commercial research purposes . Our Hindi version of Visual Genome also allows to create Hindi image labelers or other practical tools . Two experiments investigated the interaction of intonation boundaries and verb bias during spoken language comprehension in English . An online cross-modal naming task proved to be sensitive to the interaction of prosodic , syntactic , and semantic representations during resolution of a temporary syntactic closure ambiguity ( e.g. , Whenever the lady checks the room ... ) . In this paper , the main focus is on the English-to-Hindi Named-Entity transliteration and a hybrid approach is proposed . The main problem arises during the transliteration of English-Hindi is the possibility of combination of English alphabets to Hindi akshara . There is no specific rule set has been designed to convert Hindi akshara to particular English syllable yet .
Results on three public datasets show that SHTM performs much better than LSTM on the measures of mean precision and BLEU score . In addition , we utilize SHTM model to do question answering in the fashion of text generation and verifying its superiority .
question asking is a way for the communication of tacher and students in English reading teaching . Well designed questions take the students to a wonderful world , promoting the students to think actively , cultivating their ability to solve problems .
The study confirms that students do acquire the skill of question-asking during PBL tutorials . However , to maximize the benefits of question-asking for epistemological and ontological development , more innovative learning activities must be designed and realised in tutorials .
Word similarity computing is widely used in many fields , such as question answer , text clustering and so on . This paper synthesizes previous research achievement and proposes an improved word similarity computing method based on Hownet .
Information Extraction techniques help identifying certain kinds of information , but their capabilities are quite domain dependent and limited to entities with predefined patterns . Neither of these techniques is sufficient to address the Question Answering problem , but we have explored a way of combining them to build a complete question answering system . The approach The meaning of a document might be expressed in terms of entities and relations between them .
From that viewpoint , finding an answer consists not only in finding relevant documents but also in extracting relevant parts . This leads us to express the question-answering problem in terms of an information retrieval problem that can be solved using natural language processing ( NLP ) approaches . In my talk , I will focus on defining what a " good " answer is , and how a system can find it .
Further , since conversations have a natural temporal nature , we adopt the temporal nature through recurrent network . Memory networks has been successful in several NLP areas , including question answering , machine translation , speech recognition , and so on . Thus , hazarika - EtAl:2018:N18 - 1 hazarika - EtAl:2018:N18 - 1 used memory networks for emotion recognition in dyadic conversations , where two distinct memory networks enabled inter - speaker interaction , yielding state - of - the - art performance .
Our word vectors are learned functions of the internal states of a deep bidirectional language model ( biLM ) , which is pre - trained on a large text corpus . We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems , including question answering , textual entailment and sentiment analysis . We also present an analysis showing that exposing the deep internals of the pre - trained network is crucial , allowing downstream models to mix different types of semi - supervision signals .
FastHybrid is a light-weight model that requires little tuning and adaptation across different domains . It combines a fast deep model ( which will be introduced in the method section ) with an initial information retrieval model to effectively and efficiently handle answer selection . We introduce a new efficient attention mechanism in the hybrid model and demonstrate its effectiveness on several QA datasets .
An independent model can not comprehend the syntactic and semantic features of questions and answers with a small training dataset . To fill this gap , language models can be employed in implementing the answer selection part . This action enables the model to have a better understanding of the language in order to understand questions and answers better than previous works .
Using a more robust classifier also enhances the effect of the language model on the answer selection component . The results demonstrate that language comprehension is an essential requirement in natural language processing tasks such as answer-selection .
This paper describes the utility of semantic resources such as the Web , WordNet and gazetteers in the answer selection process for a question-answering system . In contrast with previous work using individual semantic resources to support answer selection , our work combines multiple resources to boost the confidence scores assigned to correct answers and evaluates different combination strategies based on unweighted sums , weighted linear combinations , and logistic regression .
Multi-Modal Similarity Metric Learning based Deep Convolutional Network for Answer Selection . Recent works using artificial neural networks based on distributed word representation greatly boost performance on various natural language processing tasks , especially the answer selection problem . Nevertheless , most of the previous works used deep learning methods ( like LSTM-RNN , CNN , etc . ) only to capture semantic representation of each sentence separately , without considering the interdependence between each other . In this paper , we propose a novel end-to-end learning framework which constitutes deep convolutional neural network based on multi-modal similarity metric learning ( M$^2$S-Net ) on pairwise tokens . The proposed model demonstrates its performance by surpassing previous state-of-the-art systems on the answer selection benchmark , i.e. , TREC-QA dataset , in both MAP and MRR metrics .
In this paper , the answer selection problem in community question answering ( CQA ) is regarded as an answer sequence labeling task , and a novel approach is proposed based on the recurrent architecture for this problem . Our approach applies convolution neural networks ( CNNs ) to learning the joint representation of question-answer pair firstly , and then uses the joint representation as input of the long short-term memory ( LSTM ) to learn the answer sequence of a question for labeling the matching quality of each answer .
In Community-based Question Answering system(CQA ) , Answer Selection(AS ) is a critical task , which focuses on finding a suitable answer within a list of candidate answers . For neural network models , the key issue is how to model the representations of QA text pairs and calculate the interactions between them .
Extensive experiments on a real-world dataset have well-validated our model . Meanwhile , we have successfully applied our proposed model to factoid question answering systems for answer candidate selection , which further demonstrates the applicability of the TRANSFER model .
A multilevel method is fulfilled to retrieve a candidate Bi-sentence . As to answer selecting , we proposed a voting method . We figure out the performance of each module , and our study shows that 65.54 % information has lost in document retrieval and Bi-sentence retrieval .
Our research focuses dialogue-based MRC , where the passages are multi-turn dialogues . It suffers from two challenges , the answer selection decision is made without support of latently helpful commonsense , and the multi-turn context may hide considerable irrelevant information . This work thus makes the first attempt to tackle those two challenges by extracting substantially important turns and utilizing external knowledge to enhance the representation of context .
Attention mechanism can then be applied to both structure and node function . We implemented and evaluated a binary - tree model of NTI , showing the model achieved the state - of - the - art performance on three different NLP tasks : natural language inference , answer sentence selection , and sentence classification , outperforming state - of - the - art recurrent and recursive neural networks . section : Introduction
section : Neural Answer Selection Model Answer sentence selection is a question answering paradigm where a model must identify the correct sentences answering a factual question from a set of candidate sentences . Assume a question is associated with a set of answer sentences , together with their judgements , where if the answer is correct and otherwise .
This paper introduced a deep neural variational inference framework for generative models of text . We experimented on two diverse tasks , document modelling and question answer selection tasks to demonstrate the effectiveness of this framework , where in both cases our models achieve state of the art performance . Apart from the promising results , our model also has the advantages of ( 1 ) simple , expressive , and efficient when training with the SGVB algorithm ; ( 2 ) suitable for both unsupervised and supervised learning tasks ; and ( 3 ) capable of generalising to incorporate any type of neural network .
By combining additional features , could our model outperform state - of - the - art models for question answering ? To address these research questions , we analyze the existing current deep learning architectures for answer ranking and make the following two key observations : Architectures not specifically designed for question / answer matching : Some methods employ CNNs for question / answer matching . However , CNNs are originally designed for computer vision ( CV ) , which uses position - shared weights with local perceptive filters , to learn spatial regularities in many CV tasks .
With an additional simple feature like QL , our model is significantly better than the previous state - of - the - art methods including deep learning methods . Answer Ranking in CQA . There is also previous research on ranking answers from community question answering ( CQA ) sites .
Our model , the Gated - Attention ( GA ) Reader , integrates a multi - hop architecture with a novel attention mechanism , which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader . This enables the reader to build query - specific representations of tokens in the document for accurate answer selection . The GA Reader obtains state - of - the - art results on three benchmarks for this task – the CNN & Daily Mail news stories and the Who
“ question”s created by replacing the answer in a sentence from the text with blank . For the answer selection task this paper focuses on , several datasets exist , e.g. TREC - QA for factoid answer extraction from multiple given passages , bAbI designed for inference purpose , and the SQuAD dataset used in this paper . To the best of our knowledge , the SQuAD dataset is the only one for both factoid and non - factoid answer extraction with a question distribution more close to real - world applications .
For instance , gated attention model employs a multi - layer architecture , where each layer encodes the same document , but the attention is updated from layer to layer . EpiReader adopted a joint training model for answer extractor and reasoner , where the extractor proposes top candidates , and the reasoner weighs each candidate by examining entailment relationship between question - answer representation and the document . An iterative alternating attention mechanism and gating strategies were proposed in to optimize the attention through several hops .
section : 21 section : APPENDIX VIII : ANSWER DISTRIBUTION The top 250 answers in our real images dataset along with their counts and percentage counts are given below .
Recent years have seen rapid growth in the MRC community . With the release of various datasets , the MRC task has evolved from the early cloze - style test to answer extraction from a single passage and to the latest more complex question answering on web data . Great efforts have also been made to develop models for these MRC tasks , especially for the answer extraction on single passage .
From Table [ reference ] , we can see that the answer verification makes a great contribution to the overall improvement , which confirms our hypothesis that cross - passage answer verification is useful for the multi - passage MRC . For the ablation of the content model , we analyze that it will not only affect the content score itself , but also violate the verification model since the content probabilities are necessary for the answer representation , which will be further analyzed in Section [ reference ] . Another discovery is that jointly training the three models can provide great benefits , which shows that the three tasks are actually closely related and can boost each other with shared representations at bottom layers .
Both benchmarks are organized around the task of answer sentence selection , where a system must identify the sentence containing the correct answer in a collection of documents , but need not return the actual answer as a KB - based system would do . Unfortunately , these datasets are very small ( hundreds of examples ) and , because of their answer selection setting , do not offer the option to directly compare answering from a KB against answering from pure text . Using similar resources as the dialog dataset of , our new benchmark WikiMovies addresses both deficiencies by providing a substantial corpus of question - answer pairs that can be answered by either using a KB or a corresponding set of documents .
Open domain question answering is slowly broadening its horizons , expanding from simple factoid questions to encompass broader and more complex queries . Recent areas of interest include scenario-based question answering , incorporating existing domain knowledge during question analysis , various types of inference , and intelligently processing subquestions .
Starting from these observations , this paper addresses the problem of enhancing interactions between non-expert users and data available on the Web . In particular , we present QAKiS , a system for open domain Question Answering over linked data , that addresses the problem of question interpretation as a relation-based match , where fragments of the question are matched to binary relations of the triple store , using relational textual patterns automatically collected . In the current version , the relational patterns are automatically extracted from Wikipedia , while DBpedia is the data set to be queried using a natural language interface .
Applications in business intelligence , health care , customer support , enterprise knowledge management , social computing , science and government would all benefit from such technology . The Project Watson is addressing a grand challenge in Computer Science aimed at illustrating how the integration and advancement of Natural Language Processing ( NLP ) , Information Retrieval ( IR ) , Machine Learning ( ML ) , massively parallel computation and Knowledge Representation and Reasoning ( KR&R ) can advance open-domain automatic Question Answering to a point where it clearly and consistently rivals the best human performance . An exciting proof-point in this challenge is to develop a computer system that can successfully compete against top human players at the well-known Jeopardy !
Automatic open-domain Question Answering has been a long standing research challenge in the AI community . IBM Research undertook this challenge with the design of the DeepQA architecture and the implementation of Watson .
We introduce shapaqa , a shallow parsing approach to online , open-domain question answering on the WorldWideWeb . Given a form-based natural language question as input , the system uses a memory-based shallow parser to analyze web pages retrieved using normal keyword search on a search engine .
Question answering , which is the retrieving of answers to natural language questions from a document collection , tries to remove the onus on the end-user by providing direct access to relevant information . This thesis is concerned with open-domain complex question answering . Unlike simple questions , complex questions can not be answered easily as they often require inferencing and synthesizing information from multiple documents .
In this paper , we explore how the state of the art in answer selection has improved recently , comparing two of the best proposed models for tackling the problem : the Crossattentive Convolutional Network and the BERT model . The experiments are carried out on two datasets , WikiQA and SelQA , both created for and used in open-domain question answering challenges . We also report on cross domain experiments with the two datasets .
Text embedding representing natural language documents in a semantic vector space can be used for document retrieval using nearest neighbor lookup . In order to study the feasibility of neural models specialized for retrieval in a semantically meaningful way , we suggest the use of the Stanford Question Answering Dataset ( SQuAD ) in an open-domain question answering context , where the first task is to find paragraphs useful for answering a given question . First , we compare the quality of various text-embedding methods on the performance of retrieval and give an extensive empirical comparison on the performance of various non-augmented base embedding with , and without IDF weighting .
Answer selection is a crucial subtask of the open domain question answering problem . In this paper , we introduce the Bi-directional Gated Memory Network ( BGMN ) to model the interactions between question and answer .
We describe a robust accurate domain-independent approach to statistical parsing incorporated into the new release of the ANLT toolkit , and publicly available as a research tool . The system has been used to parse many well known corpora in order to produce data for lexical acquisition efforts ; it has also been used as a component in an open-domain question answering project . The performance of the system is competitive with that of statistical parsers using highly lexicalised parse selection models .
In this paper , we propose a new method to generate multi-resolution word embeddings that represent documents at multiple resolutions in terms of context-scopes . In order to investigate its performance , we use the Stanford Question Answering Dataset ( SQuAD ) and the Question Answering by Search And Reading ( QUASAR ) in an open-domain question-answering setting , where the first task is to find documents useful for answering a given question . To this end , we first compare the quality of various text-embedding methods for retrieval performance and give an extensive empirical comparison with the performance of various non-augmented base embeddings with and without multi-resolution representation .
Teaching machines how to automatically answer questions asked in natural language on any topic or in any domain has always been a long standing goal in Artificial Intelligence . With the rise of large scale structured knowledge bases ( KBs ) , this problem , known as open - domain question answering ( or open QA ) , boils down to being able to query efficiently such databases with natural language . These KBs , such as Freebase [ reference ] encompass huge ever growing amounts of information and ease open QA by organizing a great variety of answers in a structured format .
Syllables are concatenated to generate a synthesized Malayalam speech . Machine translation of English to Malayalam text is tested and achieved 73 percentage accuracy . For the TTS system , accuracy is verified by checking the naturalness and intelligibility .
Normalization process calculates equal number of words in English-Hindi sentences to find exact alignment of each word . Second strategy is mapping English-Hindi sentence using parallel English-Hindi word dictionary . Parallel English-Hindi dictionary contains normalized English-Hindi word , which are more able to align than previous other approach of alignment .
First is normalization of tagged English sentences and Hindi sentences . Second is mapping English-Hindi sentence using parallel English-Hindi word dictionary . Hence proposed system is desirable to encourage English and Hindi parallel sentences .
Code mixing and switching is natural in bilingual and multilingual settings in general and bilingual Hindi English speech community in particular . This book belies the fact that code-mixing and code-switching is not arbitrary , in fact it is natural and patterned .
This study aimed to examine the role played by the depth of vocabulary knowledge in the reading comprehension process of Korean EFL learners within the framework of the Simple View of Reading ( SVR ) ( Gough & Tunmer , 1986 ) . In addition to the two major SVR components , decoding and linguistic comprehension of English words , vocabulary depth , was added to the model to see whether it exerted any additional influence on the reading comprehension of Korean high school EFL learners . A total of 207 11th grade high school students in South Korea participated in this study by taking vocabulary breadth , vocabulary depth ( analogy , antonym , and synonym ) , listening comprehension , decoding , and reading comprehension tests .
This paper reports on a research project to design a system for automatically interpreting English specifications of digital systems in terms of design representation formalisms currently employed in CAD systems . The necessary processes involve the machine analysis of English and the synthesis of models from the specifications . The approach being investigated is interactive and consists of syntactic scanning , semantic analysis , interpretation generation , and model integration .
Abstract This article examines the status of constructed controlled terminologies from the perspective of the coverage of terms/concepts . To facilitate controlled authoring of Japanese texts of the municipal domain and promote machine translatability into English , we constructed terminologies in the following way : ( 1 ) Japanese-English term pairs are extracted from aligned texts ; ( 2 ) term variations are controlled by defining preferred and proscribed terms for both languages . To assess the coverage of the constructed terminologies , we propose a quantitative extrapolation method that estimates the potential vocabulary size .
This challenge test set was created by searching for ( particularly ) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity . Our dataset is the first for multimodal English-Hindi machine translation , freely available for non-commercial research purposes . Our Hindi version of Visual Genome also allows to create Hindi image labelers or other practical tools .
Two experiments investigated the interaction of intonation boundaries and verb bias during spoken language comprehension in English . An online cross-modal naming task proved to be sensitive to the interaction of prosodic , syntactic , and semantic representations during resolution of a temporary syntactic closure ambiguity ( e.g. , Whenever the lady checks the room ... ) .
In this paper , the main focus is on the English-to-Hindi Named-Entity transliteration and a hybrid approach is proposed . The main problem arises during the transliteration of English-Hindi is the possibility of combination of English alphabets to Hindi akshara . There is no specific rule set has been designed to convert Hindi akshara to particular English syllable yet .
SHTM: A neocortex-inspired algorithm for one-shot text generation Question Asking in English Reading Teaching Question-Asking Patterns during Problem-Based Learning Tutorials: Formal Functional Roles The Analysis and Improvement about Word Similarity Computing Method Based on HowNet The PISAB Question Answering System Finding an answer to a question DialogueRNN: An Attentive RNN for Emotion Detection in Conversations Deep contextualized word representations FastHybrid: A Hybrid Model for Efficient Answer Selection BAS: An Answer Selection Method Using BERT Language Model Exploiting Multiple Semantic Resources for Answer Selection M$^2$S-Net: Multi-Modal Similarity Metric Learning based Deep Convolutional Network for Answer Selection Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering Sequential Attention with Keyword Mask Model for Community-based Question Answering I Know What You Want to Express: Sentence Element Inference by Incorporating External Knowledge Base TREC 2003 Question Answering Track at CAS-ICT Multi-choice Dialogue-Based Reading Comprehension with Knowledge and Key Turns Neural Tree Indexers for Text Understanding Neural Variational Inference for Text Processing aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model Gated-Attention Readers for Text Comprehension End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension VQA: Visual Question Answering Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification Key-Value Memory Networks for Directly Reading Documents FLOOD: A Planning Framework for Reasoning with Linguistic Data Natural language interaction with the web of data by mining its textual side Building Watson: A Grand Challenge in Automatic NL Question Answering Predicting Lexical Answer Types in Open Domain QA SHAPAQA : Shallow Parsing for Question Answering on the World Wide Web Improvements to the complex question answering models A Comparative Study of Models for Answer Sentence Selection Text Embeddings for Retrieval From a Large Knowledge Base Bi-directional Gated Memory Networks for Answer Selection Robust Accurate Statistical Annotation of General Text A Multi-Resolution Word Embedding for Document Retrieval from Large Unstructured Knowledge Bases Question Answering with Subgraph Embeddings Text to speech synthesis system for English to Malayalam translation Alignment of English-Hindi Sentences Knowledge of Word Alignment Position in English-Hindi Sentences Code Mixing and Switching: Syntactic Constraints Unique Contribution of Vocabulary Depth to Korean EFL Learners’ Reading Comprehension Toward Synthesis from English Descriptions Building controlled bilingual terminologies for the municipal domain and evaluating them using a coverage estimation approach Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation Functions of intonation boundaries during spoken language comprehension in English A HYBRID APPROACH OF ENGLISH- HINDI NAMED-ENTITY TRANSLITERATION
918c36d15f60da04927bd6c1645a8d09611535bf 808611074562447a61681f8e68b0b8d3cafcbb7d 581b02479637e927ab04ebc3808fccfd467aa26d 8b51dc4a3a243f4cb81557e7d85f14dc15a05157 620e797f4bd23f2343a240f6c7e84a78be39bdd1 1206383595a741f7927e815caa888d696fdd874f 5708b4965a581408cf7cab46a29d89d4f674f7f9 6987ce1b4aafca7fbb5b3a2160b463df262d550d 56aa2578c3c1d67f5cc078ab7800085d0b413b58 29990c3f2d38b052414886bae5f35cbac8224033 ca607fba860ea2c9a80f780e6e1d3d3c6a61a4e0 5453338c0dd15aab4dab4d1e3989494437aaaf3b 31e17c7eb97100831cc96388894cbc62abbd64b8 98156fe5f601e576da64ff3a0178988f752f8868 80405b65ae55217086c8529d74f000b10cc9f2fd 37aabd8acd68fdcf2595691ac1261b23a86f2905 8e7981f69624ecebe8a6f47bad8e92c47ad4fbca dd6481c0d20e4a298bdb25e20a20ebee2280b9e9 ee8aa31ce672960a1a313d2b3d91c10104360b9b 87bda45d6fad233815360c63314070f0e9e557e9 4fa7a676aceb4aa1feda172a0765dddd48e40f59 af78c45ee23f22d9b9dbfb358e9626133c8c5789 a2c39877b4e9ec87c86e222e656e738dc2569948 0ab7c20a6762fdf901bfc22f3cf8f73150ca0090 d8fe99b6dc76dc342fe9fb47740fee40381fa13d c494941a0fd70712c5ebc64720276c68c84e0fa1 d3a6609fb2839f76e47f90855d68995312cb6040 fb1303db06efff9687b07cfe1e14ca74d1ffbe27 caf41627d323d4f1451d6bcf56b537a3174e3e98 e9d7ad9097a5452fb83fd4f894adbe45cf7b3c4c 5a38617b5807496bbe14c9a5f1944790aaef6b57 d01abcccb59fd17f11fc54d19ccf363835adbe01 9a94508c82354e29762eb96a84a5e378e1cc55b7 5a64c71e8f29a3b7ef77ab24daf2eca3b73f4a75 570a5d4255eb1700cebd629b6c1fe48f460a15b4 110d4dfa4187e11dab867dc9ee159d229aa77af3
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2016 2008 2014 2014 2000 2006 2019 2018 2016 2019 2006 2016 2015 2019 2017 2003 2020 2017 2016 2016 2017 2017 2015 2018 2016 2003 2012 2010 2012 2001 2011 2019 2018 2017 2002 2019 2014 2016 2012 2012 2014 1989 2018 2019 2004 2015
SMC TREC IWRIDL AAAI NAACL-HLT COLING LREC NAACL-HLT (1) TREC EACL ICML CIKM ACL 2015 IEEE International Conference on Computer Vision (ICCV) ACL EMNLP NTCIR CLiC-it CCL LREC EMNLP DAC INTERSPEECH
https://api.semanticscholar.org/918c36d15f60da04927bd6c1645a8d09611535bf https://api.semanticscholar.org/808611074562447a61681f8e68b0b8d3cafcbb7d https://api.semanticscholar.org/581b02479637e927ab04ebc3808fccfd467aa26d https://api.semanticscholar.org/8b51dc4a3a243f4cb81557e7d85f14dc15a05157 https://api.semanticscholar.org/620e797f4bd23f2343a240f6c7e84a78be39bdd1 https://api.semanticscholar.org/1206383595a741f7927e815caa888d696fdd874f https://www.semanticscholar.org/paper/3a7895b17db0cda7bbf86bcda52c46a3e03b6ded https://www.semanticscholar.org/paper/3febb2bed8865945e7fddc99efd791887bb7e14f https://api.semanticscholar.org/5708b4965a581408cf7cab46a29d89d4f674f7f9 https://api.semanticscholar.org/6987ce1b4aafca7fbb5b3a2160b463df262d550d https://api.semanticscholar.org/56aa2578c3c1d67f5cc078ab7800085d0b413b58 https://api.semanticscholar.org/29990c3f2d38b052414886bae5f35cbac8224033 https://api.semanticscholar.org/ca607fba860ea2c9a80f780e6e1d3d3c6a61a4e0 https://api.semanticscholar.org/5453338c0dd15aab4dab4d1e3989494437aaaf3b https://api.semanticscholar.org/31e17c7eb97100831cc96388894cbc62abbd64b8 https://api.semanticscholar.org/98156fe5f601e576da64ff3a0178988f752f8868 https://api.semanticscholar.org/80405b65ae55217086c8529d74f000b10cc9f2fd https://www.semanticscholar.org/paper/705dcc8eadba137834e4b0359e2d696d4b209f5b https://www.semanticscholar.org/paper/73e8633886dc380a46fc02f2e1ec5bf68dba0734 https://www.semanticscholar.org/paper/364c1a3df58d87cb40ab33fdf3831cf2862f3570 https://www.semanticscholar.org/paper/0f2ea810c16275dc74e880296e20dbd83b1bae1c https://www.semanticscholar.org/paper/0680f04750b1e257ffdd161e85382031dc73ea7f https://www.semanticscholar.org/paper/97ad70a9fa3f99adf18030e5e38ebe3d90daa2db https://www.semanticscholar.org/paper/0985497d1de3ffd11713e75289cc2ad55836623d https://www.semanticscholar.org/paper/bba5f2852b1db8a18004eb7328efa5e1d57cc62a https://api.semanticscholar.org/37aabd8acd68fdcf2595691ac1261b23a86f2905 https://api.semanticscholar.org/8e7981f69624ecebe8a6f47bad8e92c47ad4fbca https://api.semanticscholar.org/dd6481c0d20e4a298bdb25e20a20ebee2280b9e9 https://api.semanticscholar.org/ee8aa31ce672960a1a313d2b3d91c10104360b9b https://api.semanticscholar.org/87bda45d6fad233815360c63314070f0e9e557e9 https://api.semanticscholar.org/4fa7a676aceb4aa1feda172a0765dddd48e40f59 https://api.semanticscholar.org/af78c45ee23f22d9b9dbfb358e9626133c8c5789 https://api.semanticscholar.org/a2c39877b4e9ec87c86e222e656e738dc2569948 https://api.semanticscholar.org/0ab7c20a6762fdf901bfc22f3cf8f73150ca0090 https://api.semanticscholar.org/d8fe99b6dc76dc342fe9fb47740fee40381fa13d https://api.semanticscholar.org/c494941a0fd70712c5ebc64720276c68c84e0fa1 https://www.semanticscholar.org/paper/33261d252218007147a71e40f8367ed152fa2fe0 https://api.semanticscholar.org/d3a6609fb2839f76e47f90855d68995312cb6040 https://api.semanticscholar.org/fb1303db06efff9687b07cfe1e14ca74d1ffbe27 https://api.semanticscholar.org/caf41627d323d4f1451d6bcf56b537a3174e3e98 https://api.semanticscholar.org/e9d7ad9097a5452fb83fd4f894adbe45cf7b3c4c https://api.semanticscholar.org/5a38617b5807496bbe14c9a5f1944790aaef6b57 https://api.semanticscholar.org/d01abcccb59fd17f11fc54d19ccf363835adbe01 https://api.semanticscholar.org/9a94508c82354e29762eb96a84a5e378e1cc55b7 https://api.semanticscholar.org/5a64c71e8f29a3b7ef77ab24daf2eca3b73f4a75 https://api.semanticscholar.org/570a5d4255eb1700cebd629b6c1fe48f460a15b4 https://api.semanticscholar.org/110d4dfa4187e11dab867dc9ee159d229aa77af3