This data belongs to the CLEF 2009 track on QA on speech transcripts ( QAst ) . Index Terms : question answering , answer extraction , oral question answering , speech transcriptions 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 . There are various uses to the knowledge source for the Tutorial System that is interesting and important to the expert system fields in an Artificial Intelligent development in the previous research studies . However we are interested in the questioning answering system which is applied to use the answering question test system in the formats of the previous exam question solving to the new exam question solving so that it can be given from the background knowledge in various ways such as the heuristics , experiences , textbooks and expertise , etc . The main point of our research is how we could give the knowledge from all of the sources to help for solving a new problem . The writer used some techniques in teaching English reading . They are : questionanswer , reading aloud , dialogue , games , intensive reading and translation . The writer applied the techniques into the stages of reading development . Moreover , preschool children are incessantly inquisitive , and will readily engage in question answering and asking activities if given the opportunity . We argue here that question asking/answering technologies can play a major role in early literacy . We describe the design and evaluation of a conversational agent called Spot , with the goal of engaging children in a 20-questions game . In this paper , we propose two new methods targeting NTCIR-4 QAC2 . First , we use knowledge resembling “ common sense ” for question answering purposes . For example , the length of a runway in an airport must be a few kilometers , but a few centimeters . This has created prominent success and a spike in work on question answering . This paper discusses the successes and challenges in question answering question answering systems and techniques that are used in these challenges . For example , an image understanding system that does not recognize an arrest event in a zoomed image of a person 's face . How is it possible to know there is more information , outside of what the system can access , that affects the interpretation of data The solution to the context problem in practice today is a pragmatic , engineering one : analyze errors ( in recommendations , question answers , image recognition , etc . ) , classify the kinds of contextual information that caused the wrong behavior , find the most common type of context that causes errors , and add information about that kind of context to the system . Clearly this approach is neither general nor scalable , and ignores the infamous long tail of possible contextual information that may affect a system 's understanding and its behavior . Shallow natural language processingmethods seem to work well on a limited rangeof questions , but they are not suitable for ﬁndinganswers to more complex questions . This paper describes work in progress on thequestion answering system IRSAW section : Interactive Visual QA Demo Question answering is essentially an interactive activity , thus it would be good to make the trained models able to interact with people in real time . Aided by the simplicity of the baseline model , we built a web demo that people could type question about a given image and our AI system powered by iBOWIMG will reply the most possible answers . [ reference ] ) : where is a matrix and its i - th column is the visual feature vector for the region indexed by . subsection : Question Model As show that LSTMs and CNNs are powerful to capture the semantic meaning of texts , we explore both models for question representations in this study . At the question level , we use recurrent neural networks to encode the entire question . For each level of the question representation in this hierarchy , we construct joint question and image co - attention maps , which are then combined recursively to ultimately predict a distribution over the answers . Overall , the main contributions of our work are : We propose a novel co - attention mechanism for VQA that jointly performs question - guided visual attention and image - guided question attention . Machine reading comprehension ( MRC ) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine . Compared with MRC on a single passage , multi - passage MRC is more challenging , since we are likely to get multiple confusing answer candidates from different passages . We used publicly available benchmark datasets from to evaluate EXAM . There are in total 6 text classification datasets , corresponding to sentiment analysis , news classification , question - answer and ontology extraction tasks , respectively . Table 1 shows the descriptive statistics of datasets used in our experiments . subsection : Question Answering Table [ reference ] presents our results on two question answering benchmarks . The results demonstrate that the introduction of the refinement module helps consistently , and further improvements come from using common sense knowledge from ConceptNet ( ) . 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 . We present a new , simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models . This makes the model particularly suitable for question - answering problems where the answer is a single word from the document . blackEnsemble of our models sets new state of the art on all evaluated datasets . paragraph : Binary and multi - class classification We use a set of binary classification tasks ( see Table [ reference ] ) that covers various types of sentence classification , including sentiment analysis ( MR , SST ) , question - type ( TREC ) , product reviews ( CR ) , subjectivity / objectivity ( SUBJ ) and opinion polarity ( MPQA ) . We generate sentence vectors and train a logistic regression on top . This paper describes a data structure , MENS ( MEmory Net Structure ) , that is useful for storing semantic information stemming from a natural language , and a system , MENTAL ( MEmory Net That Answers and Learns ) that interacts with a user ( human or program ) , stores information into and retrieves information from MENS and interprets some information in MENS as rules telling it how to deduce new information from what is already stored . MENTAL can be used as a guestion-answering system with formatted input /output , as a vehicle for experimenting with various theories of semantic structures or as the memory management portion of a natural language question-answering system . We address the task of answering natural language questions by using the large number of Frequently Asked Questions ( FAQ ) pages available on the web . The task involves three steps : ( 1 ) fetching FAQ pages from the web ; ( 2 ) automatic extraction of question/answer ( Q/A ) pairs from the collected pages ; and ( 3 ) answering users ' questions by retrieving appropriate Q/A pairs . In this paper , we describe automatic evaluation method for question answering in natural language . This method is based on BEs ( Basic Elements ) originally proposed by Hovy et . In addition to learning good practices and reusing code from mining code examples , programmers can be supported in their learning and code improvement processes through negative , or poorly written , code examples . While it is challenging to identify negative code examples automatically from within source code , we leverage a key insight that the natural language in questions that include code examples posted on forums can provide adequate clues . In this paper , we describe an automatic sentiment analysis-based technique for mining negative code examples from developer question and answer forums along with a technique to automatically mine negative sentiment indicators commonly used by developers , which are used to drive the sentiment-based technique . The paper proposes a model of intelligent Q/A system which supports natural language question asking according to the problems from the existing intelligent Q/A systems at home and abroad . When user asks question , the system makes use of relevant technology to comprehend the natural language text automatically that user inputs , and makes information matching in database , and then returns the answers associated with problems and gives a feedback . Search queries have evolved beyond keyword queries . Many complex queries such as verbose queries , natural language question queries and document-based queries are widely used in a variety of applications . Processing these complex queries usually requires a series of query operations , which results in multiple sequences of reformulated queries . However , automatically extracting the answer from those texts remains an open challenge . The recent success of neural models at answering questions given a related paragraph suggests neural models have the potential to be a key part of a solution to this problem . Training and testing neural models that take entire documents as input is extremely computationally expensive , section : Introduction Two grand challenges in artificial intelligence research have been to build models that can make multiple computational steps in the service of answering a question or completing a task , and models that can describe long term dependencies in sequential data . Recently there has been a resurgence in models of computation using explicit storage and a notion of attention ; manipulating such a storage offers an approach to both of these challenges . Yu18 , DevlinCLT18 . The demand for answering questions in natural language is increasing rapidly , and this has led to the development of smart devices such as Siri and Alexa . However , in comparison with answer span extraction , the natural language generation Our framework also exploits both CNN and RNNs , but in contrast to preceding approaches which use only image features extracted from a CNN in answering a question , we employ multiple sources , including image content , generated image captions and mined external knowledge , to feed to an RNN to answer questions . Large - scale Knowledge Bases ( KBs ) , such as Freebase and DBpedia , have been used successfully in several natural language Question Answering ( QA ) systems . However , VQA systems exploiting KBs are still relatively rare . Nevertheless , a considerable number of research illustrates ESL students face difficulties in understanding the reading materials assigned to them . The current approach which focuses on students lifting specific information in the text to answer the reading comprehension questions at the end of the text has hindered students ’ understanding of the text as well as not permitting students to experience reading as meaningful and engaging activity . Thus , this exploratory study attempts to bridge the gap by employing graphic organizers as a tool to facilitate students ’ reading comprehension skill . The organizers introduced the Schema-Guided Dialogue ( SGD ) dataset with multi-domain conversations and released a zero-shot dialogue state tracking model . In this work , we propose a GOaL-Oriented Multi-task BERT-based dialogue state tracker ( GOLOMB ) inspired by architectures for reading comprehension question answering systems . The model " queries " dialogue history with descriptions of slots and services as well as possible values of slots . This paper describes a novel hierarchical attention network for reading comprehension style question answering , which aims to answer questions for a given narrative paragraph . In the proposed method , attention and fusion are conducted horizontally and vertically across layers at different levels of granularity between question and paragraph . We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit . In contrast to most question answering and reading comprehension models today , which operate over small amounts of input text , our system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles in an end-to-end fashion . We report large improvements over previous results on a standard benchmark test collection , showing that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identifying answer spans . ( 2 ) Abdul Manan , M. Sc . , M. A. , Ph . D. Key words : test , reading comprehension question types This study entitled “ Analyzing Students ’ Difficulties in Reading Comprehension Test ” was intended to ( 1 ) find out the most difficult reading comprehension question type faced by the students in reading test and ( 2 ) why the students faced the difficulties in that question . The sample of this study was the students of XIIIPS-4 and there were 24 students of XIIIPS-4 . After analyzing the general features of reading comprehensio n test que stions , the author discusses respectively the features of the 4 frequently-used reading comprehension test questions , namely , multiple choice , true or false jud gment , answering question , and fill-in blanks . section : Introduction Reading comprehension - based question answering ( RCQA ) is the task of answering a question with a chunk of text taken from related document ( s ) . A variety of neural models have been proposed recently either for extracting a single entity or a single token as an answer from a given text ; or for selecting the correct answer by ranking a small set of human - provided candidates . The evaluation showed that it is possible to recognize valid pairs in 70 % of cases in the domains of weak request-response agreement and 80 % of cases in the domains of strong agreement , which is essential to support automated conversations . These accuracies are comparable with the benchmark task of classification of discourse trees themselves as valid or invalid , and also with classification of multi-sentence answers in factoid question-answering systems . The applicability of proposed machinery to the problem of chatbots , social chats and programming via NL is demonstrated . Factoid questions are questions that require short fact-based answers . Automatic generation ( AQG ) of factoid questions from a given text can contribute to educational activities , interactive question answering systems , search engines , and other applications . While much recent progress has been made in research on fact-based question answering , our work aims to extend question-answering research in a different direction ─ to handle multi-perspective question-answering tasks , i.e. question-answering tasks that require an ability to find and organize opinions in text . In particular , this paper proposes an approach to multi-perspective question answering that views the task as one of opinion-oriented information extraction . Question answering is one of the primary challenges of natural language understanding . In realizing such a system , providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation . The different methods explored in the literature can be broadly classified into three categories namely : 1 ) classification based , 2 ) knowledge graph based and 3 ) retrieval based . Generally it is hard to obtain explicit evidence to conclude a proposition is false from an information source , so we convert this task to a set of factoid-style questions and use an existing question answering system as a subsystem . By aggregating the answers and confidence values from a factoid-style question answering system we can determine the correctness of the entire proposition or the substitutions that make the proposition false . We evaluated the system on multiple-choice questions from a university admission test on world history , and found it to be highly accurate . Furthermore , a text-based opinion question may be short and vague , while the video answers could be verbal , less structured grammatically , and noisy because of errors in speech transcription . Direct matching of words or syntactic analysis of sentence structure , such as adopted by factoid and how-to question-answering , is unlikely to find video answers . The first problem , the answer localization , is addressed by audiovisual analysis of the emotional signals in videos for locating video segments likely expressing opinions . QALab task requires to solve the history problems of the Center Exam . Although it seems like a factoid based question- answering problems , we suggest a simple , but fundamentally important , keyword based technique . Regardless of employed methods , the way how to handle the keyword distribution is the fundamental issue to solve the problems . We use semantic analogical reasoning to find the location of the final answer from a textual passage . We demonstrate that without employing any linguistic tools during the answer selection process , our approach achieves a better accuracy than a typical factoid question answering architecture . We also add attention scheme to learn question term importance . Factoid Question Answering . There have been many previous studies on factoid question answering , most of which use the benchmark data from TREC QA track . With a simple additional feature , our method can achieve the new state - of - the - art performance among current existing methods . For further work , we will study other deep learning architectures for answer ranking and extend our work to include non - factoid question answering data sets . section : Acknowledgments We conduct extensive experiments on the MS - MARCO and DuReader datasets . The results show that our answer verification MRC model outperforms the baseline models by a large margin and achieves the state - of - the - art performance on both datasets . section : Our Approach Intuitively , it is unnecessary since the underlying comprehension and reasoning of language for these components is the same . In this paper , we decompose the problem of MRC with unanswerable questions into three sub - tasks : answer pointer , no - answer pointer , and answer verifier . Since these three sub - tasks are highly related , we regard the MRC with unanswerable questions as a multi - task learning problem by sharing some meta - knowledge . Besides , is the cost of reconstructing the document from a sample , where is the average length of the documents and represents the volume of words applied in this document model , which is conventionally much lager than . The computational complexity of NASM for a training question - answer pair is . The inference network needs .
This data belongs to the CLEF 2009 track on QA on speech transcripts ( QAst ) . Index Terms : question answering , answer extraction , oral question answering , speech transcriptions
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 .
There are various uses to the knowledge source for the Tutorial System that is interesting and important to the expert system fields in an Artificial Intelligent development in the previous research studies . However we are interested in the questioning answering system which is applied to use the answering question test system in the formats of the previous exam question solving to the new exam question solving so that it can be given from the background knowledge in various ways such as the heuristics , experiences , textbooks and expertise , etc . The main point of our research is how we could give the knowledge from all of the sources to help for solving a new problem .
The writer used some techniques in teaching English reading . They are : questionanswer , reading aloud , dialogue , games , intensive reading and translation . The writer applied the techniques into the stages of reading development .
Moreover , preschool children are incessantly inquisitive , and will readily engage in question answering and asking activities if given the opportunity . We argue here that question asking/answering technologies can play a major role in early literacy . We describe the design and evaluation of a conversational agent called Spot , with the goal of engaging children in a 20-questions game .
In this paper , we propose two new methods targeting NTCIR-4 QAC2 . First , we use knowledge resembling “ common sense ” for question answering purposes . For example , the length of a runway in an airport must be a few kilometers , but a few centimeters .
This has created prominent success and a spike in work on question answering . This paper discusses the successes and challenges in question answering question answering systems and techniques that are used in these challenges .
For example , an image understanding system that does not recognize an arrest event in a zoomed image of a person 's face . How is it possible to know there is more information , outside of what the system can access , that affects the interpretation of data The solution to the context problem in practice today is a pragmatic , engineering one : analyze errors ( in recommendations , question answers , image recognition , etc . ) , classify the kinds of contextual information that caused the wrong behavior , find the most common type of context that causes errors , and add information about that kind of context to the system . Clearly this approach is neither general nor scalable , and ignores the infamous long tail of possible contextual information that may affect a system 's understanding and its behavior .
Shallow natural language processingmethods seem to work well on a limited rangeof questions , but they are not suitable for ﬁndinganswers to more complex questions . This paper describes work in progress on thequestion answering system IRSAW
section : Interactive Visual QA Demo Question answering is essentially an interactive activity , thus it would be good to make the trained models able to interact with people in real time . Aided by the simplicity of the baseline model , we built a web demo that people could type question about a given image and our AI system powered by iBOWIMG will reply the most possible answers .
[ reference ] ) : where is a matrix and its i - th column is the visual feature vector for the region indexed by . subsection : Question Model As show that LSTMs and CNNs are powerful to capture the semantic meaning of texts , we explore both models for question representations in this study .
At the question level , we use recurrent neural networks to encode the entire question . For each level of the question representation in this hierarchy , we construct joint question and image co - attention maps , which are then combined recursively to ultimately predict a distribution over the answers . Overall , the main contributions of our work are : We propose a novel co - attention mechanism for VQA that jointly performs question - guided visual attention and image - guided question attention .
Machine reading comprehension ( MRC ) on real web data usually requires the machine to answer a question by analyzing multiple passages retrieved by search engine . Compared with MRC on a single passage , multi - passage MRC is more challenging , since we are likely to get multiple confusing answer candidates from different passages .
We used publicly available benchmark datasets from to evaluate EXAM . There are in total 6 text classification datasets , corresponding to sentiment analysis , news classification , question - answer and ontology extraction tasks , respectively . Table 1 shows the descriptive statistics of datasets used in our experiments .
subsection : Question Answering Table [ reference ] presents our results on two question answering benchmarks . The results demonstrate that the introduction of the refinement module helps consistently , and further improvements come from using common sense knowledge from ConceptNet ( ) .
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 .
We present a new , simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models . This makes the model particularly suitable for question - answering problems where the answer is a single word from the document . blackEnsemble of our models sets new state of the art on all evaluated datasets .
paragraph : Binary and multi - class classification We use a set of binary classification tasks ( see Table [ reference ] ) that covers various types of sentence classification , including sentiment analysis ( MR , SST ) , question - type ( TREC ) , product reviews ( CR ) , subjectivity / objectivity ( SUBJ ) and opinion polarity ( MPQA ) . We generate sentence vectors and train a logistic regression on top .
This paper describes a data structure , MENS ( MEmory Net Structure ) , that is useful for storing semantic information stemming from a natural language , and a system , MENTAL ( MEmory Net That Answers and Learns ) that interacts with a user ( human or program ) , stores information into and retrieves information from MENS and interprets some information in MENS as rules telling it how to deduce new information from what is already stored . MENTAL can be used as a guestion-answering system with formatted input /output , as a vehicle for experimenting with various theories of semantic structures or as the memory management portion of a natural language question-answering system .
We address the task of answering natural language questions by using the large number of Frequently Asked Questions ( FAQ ) pages available on the web . The task involves three steps : ( 1 ) fetching FAQ pages from the web ; ( 2 ) automatic extraction of question/answer ( Q/A ) pairs from the collected pages ; and ( 3 ) answering users ' questions by retrieving appropriate Q/A pairs .
In this paper , we describe automatic evaluation method for question answering in natural language . This method is based on BEs ( Basic Elements ) originally proposed by Hovy et .
In addition to learning good practices and reusing code from mining code examples , programmers can be supported in their learning and code improvement processes through negative , or poorly written , code examples . While it is challenging to identify negative code examples automatically from within source code , we leverage a key insight that the natural language in questions that include code examples posted on forums can provide adequate clues . In this paper , we describe an automatic sentiment analysis-based technique for mining negative code examples from developer question and answer forums along with a technique to automatically mine negative sentiment indicators commonly used by developers , which are used to drive the sentiment-based technique .
The paper proposes a model of intelligent Q/A system which supports natural language question asking according to the problems from the existing intelligent Q/A systems at home and abroad . When user asks question , the system makes use of relevant technology to comprehend the natural language text automatically that user inputs , and makes information matching in database , and then returns the answers associated with problems and gives a feedback .
Search queries have evolved beyond keyword queries . Many complex queries such as verbose queries , natural language question queries and document-based queries are widely used in a variety of applications . Processing these complex queries usually requires a series of query operations , which results in multiple sequences of reformulated queries .
However , automatically extracting the answer from those texts remains an open challenge . The recent success of neural models at answering questions given a related paragraph suggests neural models have the potential to be a key part of a solution to this problem . Training and testing neural models that take entire documents as input is extremely computationally expensive ,
section : Introduction Two grand challenges in artificial intelligence research have been to build models that can make multiple computational steps in the service of answering a question or completing a task , and models that can describe long term dependencies in sequential data . Recently there has been a resurgence in models of computation using explicit storage and a notion of attention ; manipulating such a storage offers an approach to both of these challenges .
Yu18 , DevlinCLT18 . The demand for answering questions in natural language is increasing rapidly , and this has led to the development of smart devices such as Siri and Alexa . However , in comparison with answer span extraction , the natural language generation
Our framework also exploits both CNN and RNNs , but in contrast to preceding approaches which use only image features extracted from a CNN in answering a question , we employ multiple sources , including image content , generated image captions and mined external knowledge , to feed to an RNN to answer questions . Large - scale Knowledge Bases ( KBs ) , such as Freebase and DBpedia , have been used successfully in several natural language Question Answering ( QA ) systems . However , VQA systems exploiting KBs are still relatively rare .
Nevertheless , a considerable number of research illustrates ESL students face difficulties in understanding the reading materials assigned to them . The current approach which focuses on students lifting specific information in the text to answer the reading comprehension questions at the end of the text has hindered students ’ understanding of the text as well as not permitting students to experience reading as meaningful and engaging activity . Thus , this exploratory study attempts to bridge the gap by employing graphic organizers as a tool to facilitate students ’ reading comprehension skill .
The organizers introduced the Schema-Guided Dialogue ( SGD ) dataset with multi-domain conversations and released a zero-shot dialogue state tracking model . In this work , we propose a GOaL-Oriented Multi-task BERT-based dialogue state tracker ( GOLOMB ) inspired by architectures for reading comprehension question answering systems . The model " queries " dialogue history with descriptions of slots and services as well as possible values of slots .
This paper describes a novel hierarchical attention network for reading comprehension style question answering , which aims to answer questions for a given narrative paragraph . In the proposed method , attention and fusion are conducted horizontally and vertically across layers at different levels of granularity between question and paragraph .
We demonstrate an end-to-end question answering system that integrates BERT with the open-source Anserini information retrieval toolkit . In contrast to most question answering and reading comprehension models today , which operate over small amounts of input text , our system integrates best practices from IR with a BERT-based reader to identify answers from a large corpus of Wikipedia articles in an end-to-end fashion . We report large improvements over previous results on a standard benchmark test collection , showing that fine-tuning pretrained BERT with SQuAD is sufficient to achieve high accuracy in identifying answer spans .
( 2 ) Abdul Manan , M. Sc . , M. A. , Ph . D. Key words : test , reading comprehension question types This study entitled “ Analyzing Students ’ Difficulties in Reading Comprehension Test ” was intended to ( 1 ) find out the most difficult reading comprehension question type faced by the students in reading test and ( 2 ) why the students faced the difficulties in that question . The sample of this study was the students of XIIIPS-4 and there were 24 students of XIIIPS-4 .
After analyzing the general features of reading comprehensio n test que stions , the author discusses respectively the features of the 4 frequently-used reading comprehension test questions , namely , multiple choice , true or false jud gment , answering question , and fill-in blanks .
section : Introduction Reading comprehension - based question answering ( RCQA ) is the task of answering a question with a chunk of text taken from related document ( s ) . A variety of neural models have been proposed recently either for extracting a single entity or a single token as an answer from a given text ; or for selecting the correct answer by ranking a small set of human - provided candidates .
The evaluation showed that it is possible to recognize valid pairs in 70 % of cases in the domains of weak request-response agreement and 80 % of cases in the domains of strong agreement , which is essential to support automated conversations . These accuracies are comparable with the benchmark task of classification of discourse trees themselves as valid or invalid , and also with classification of multi-sentence answers in factoid question-answering systems . The applicability of proposed machinery to the problem of chatbots , social chats and programming via NL is demonstrated .
Factoid questions are questions that require short fact-based answers . Automatic generation ( AQG ) of factoid questions from a given text can contribute to educational activities , interactive question answering systems , search engines , and other applications .
While much recent progress has been made in research on fact-based question answering , our work aims to extend question-answering research in a different direction ─ to handle multi-perspective question-answering tasks , i.e. question-answering tasks that require an ability to find and organize opinions in text . In particular , this paper proposes an approach to multi-perspective question answering that views the task as one of opinion-oriented information extraction .
Question answering is one of the primary challenges of natural language understanding . In realizing such a system , providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation . The different methods explored in the literature can be broadly classified into three categories namely : 1 ) classification based , 2 ) knowledge graph based and 3 ) retrieval based .
Generally it is hard to obtain explicit evidence to conclude a proposition is false from an information source , so we convert this task to a set of factoid-style questions and use an existing question answering system as a subsystem . By aggregating the answers and confidence values from a factoid-style question answering system we can determine the correctness of the entire proposition or the substitutions that make the proposition false . We evaluated the system on multiple-choice questions from a university admission test on world history , and found it to be highly accurate .
Furthermore , a text-based opinion question may be short and vague , while the video answers could be verbal , less structured grammatically , and noisy because of errors in speech transcription . Direct matching of words or syntactic analysis of sentence structure , such as adopted by factoid and how-to question-answering , is unlikely to find video answers . The first problem , the answer localization , is addressed by audiovisual analysis of the emotional signals in videos for locating video segments likely expressing opinions .
QALab task requires to solve the history problems of the Center Exam . Although it seems like a factoid based question- answering problems , we suggest a simple , but fundamentally important , keyword based technique . Regardless of employed methods , the way how to handle the keyword distribution is the fundamental issue to solve the problems .
We use semantic analogical reasoning to find the location of the final answer from a textual passage . We demonstrate that without employing any linguistic tools during the answer selection process , our approach achieves a better accuracy than a typical factoid question answering architecture .
We also add attention scheme to learn question term importance . Factoid Question Answering . There have been many previous studies on factoid question answering , most of which use the benchmark data from TREC QA track .
With a simple additional feature , our method can achieve the new state - of - the - art performance among current existing methods . For further work , we will study other deep learning architectures for answer ranking and extend our work to include non - factoid question answering data sets . section : Acknowledgments
We conduct extensive experiments on the MS - MARCO and DuReader datasets . The results show that our answer verification MRC model outperforms the baseline models by a large margin and achieves the state - of - the - art performance on both datasets . section : Our Approach
Intuitively , it is unnecessary since the underlying comprehension and reasoning of language for these components is the same . In this paper , we decompose the problem of MRC with unanswerable questions into three sub - tasks : answer pointer , no - answer pointer , and answer verifier . Since these three sub - tasks are highly related , we regard the MRC with unanswerable questions as a multi - task learning problem by sharing some meta - knowledge .
Besides , is the cost of reconstructing the document from a sample , where is the average length of the documents and represents the volume of words applied in this document model , which is conventionally much lager than . The computational complexity of NASM for a training question - answer pair is . The inference network needs .
Using dependency parsing and machine learning for factoid question answering on spoken documents The PISAB Question Answering System Using Case Based Reasoning to Answer Exam Questions The Techniques Of Teaching English Reading To The Fifth Grade Students Of Sd Negeri 01 Jetis, Jaten, Karanganyar What did spot hide?: a question-answering game for preschool children Question Answering Using "Common Sense" and Utility Maximization Principle Advances in Natural Language Question Answering: A Review The Quantum Collective The Role of Information Retrieval in the Question Answering System IRSAW Simple Baseline for Visual Question Answering Stacked Attention Networks for Image Question Answering Hierarchical Question-Image Co-Attention for Visual Question Answering Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification Explicit Interaction Model towards Text Classification Dynamic Integration of Background Knowledge in Neural NLU Systems Neural Variational Inference for Text Processing Text Understanding with the Attention Sum Reader Network Supervised Learning of Universal Sentence Representations from Natural Language Inference Data A Net Structure for Semantic Information Storage, Deduction and Retrieval Retrieving answers from frequently asked questions pages on the web Automatic Evaluation of Question Answering System based on BE Method Automatically Mining Negative Code Examples from Software Developer Q & A Forums Research and Design of Intelligent Question Answering System Generating reformulation trees for complex queries Simple and Effective Multi-Paragraph Reading Comprehension End-To-End Memory Networks Multi-style Generative Reading Comprehension Image Captioning and Visual Question Answering Based on Attributes and External Knowledge Facilitating Reading Comprehension among ESL Learners Goal-Oriented Multi-Task BERT-Based Dialogue State Tracker Multi-Granularity Hierarchical Attention Fusion Networks for Reading Comprehension and Question Answering End-to-End Open-Domain Question Answering with BERTserini ANALYZING STUDENTS’ DIFFICULTIES IN READING COMPREHENSION TEST(A STUDY AT SMA NEGERI 2 LHOKSEUMAWE) Analysis of Features of Commonly-used English Reading Comprehension Test Questions End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension Rhetorical Agreement: Maintaining Cohesive Conversations A Syntactic Approach to Domain-Specific Automatic Question Generation Combining Low-Level and Summary Representations of Opinions for Multi-Perspective Question Answering Hi, how can I help you?: Automating enterprise IT support help desks Answering Yes/No Questions via Question Inversion Opinion Question Answering by Sentiment Clip Localization Solving History Exam by Keyword Distribution: KJP System at NTCIR-11 QALab Task Predicting Answer Location Using Shallow Semantic Analogical Reasoning in a Factoid Question Answering System aNMM: Ranking Short Answer Texts with Attention-Based Neural Matching Model Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification U-Net: Machine Reading Comprehension with Unanswerable Questions Neural Variational Inference for Text Processing
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INTERSPEECH TREC CHI NTCIR WWW (Companion Volume) LWA ArXiv 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS ACL AAAI ICML ACL EMNLP IJCAI CIKM ASE Workshops SIGIR ACL NIPS ACL IEEE Transactions on Pattern Analysis and Machine Intelligence ACL (1) New Directions in Question Answering COLING NTCIR PACLIC CIKM ACL ArXiv ICML
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