It is intepreted that teaching reading comprehension by using Cooperative Learning method was effective . Keywords : teaching , reading comprehension , Cooperative-Learning method Reading comprehension ability is one of the most important and elementary abilities in the English study . However , the practi cal result is not so satisfied in the English reading classes of professional colleges . This study used Kintsch ’s Construction Integration ( CI ) Model as a context for investigating reading comprehension assessment and its relationship to epistemic beliefs . Specifically , questions tied to levels of representation delineated in the CI model were used to investigate how individual differences may predict reading comprehension . How to improve Reading Comprehension has become a theme of English teaching research . This article probes how to improve reading comprehension level from schema theory . It is the basis of writing , translation , and also affects the students ' listening and speaking ability . Reading comprehension part always accounts for 40 % of the score in the English examination . However , at present , the college English reading course always can not meet the requirements of students ' good command of English reading ability . So , reading is a valuable skill , this study will concentrate on this ability . This research is focused on the study of reading comprehension rate in the school “ Cesar Andrade Cordero ” in 10th grade students afternoon shift . This study is done to provide an analysis of the reading comprehension strategies implemented by the teacher of 10th grade . Keyword : Numbered Head Together ( NHT ) , Reading Comprehension Reading is one skill that is very important in implementing teaching and learning process at Junior High School level . However , many students have difficulties in learning English , especially reading comprehension . The purpose of this study was to investigate and evaluate the reading comprehension section of Iranian UEEs from 2004 to 2013 . So the corpus of the current study included the reading passages of all groups ( Math and physics , Natural science , Humanities , and Art ) and tried to evaluate and compare the following items of the passages : text domain , test rubric ( instruction ) , number of sentences , types of sentences , and types of reading comprehension questions . The paper presents a corpus of text data and its corresponding gaze fixations obtained from autistic and non-autistic readers . The data was elicited through reading comprehension testing combined with eye-tracking recording . The corpus consists of 1034 content words tagged with their POS , syntactic role and three gaze-based measures corresponding to the autistic and control participants . Fixation duration indicated that the closest advertisements slowed down information processing but there was no difference between the intermediate ( 40 pixel ) and far ( 80 pixel ) distance conditions . Our findings suggest that advertisements have a negative impact on users ' performance mostly when a lots of cognitive resources are required as for reading-for-comprehension . However , Key - Value Memory Networks outperform all other methods on all three data source types . Reading from Wikipedia documents directly ( Doc ) outperforms an IE - based KB ( IE ) , which is an encouraging result towards automated machine reading though a gap to a human - annotated KB still remains ( 93.9 vs. 76.2 ) . The best memory representation for directly reading documents uses Question answering has been a long - standing research problem . Recently , reading comprehension ( RC ) , a challenge to answer a question given textual evidence provided in a document set , has received much attention . Here , current mainstream studies have treated RC as a process of extracting an answer span from one passage RajpurkarZLL16 , RajpurkarJL18 or multiple passages JoshiCWZ17 , which is usually done by predicting the start and end positions of the answer The theoretical framework of the proposed method is discussed in detail and two implementations are presented for the programming language ECMAScript ( JavaScript ) . The results from user testing with undergraduate students show that the proposed technique improves the student ’s learning outcomes in terms of syntax recall and logic comprehension , in comparison to traditional source code editors . , ( BT ) U.S. DEPAR TPAIMT OF HEALTH . EDUCATION & WELFARE OFFICE OF EDUCATION THIS DOCUMENT HAS BEEN REPRODUCED EXACTLY AS RECEIVED FROMTHE PERSON OR ORGANIZATION ORIGINATING IT POINTS OF VIEW OR OPINIONS STATED DO NOT NECES SARILY PEPRESENT OFFICIAL OFFICE OF EDUCATION POSITION OR POLICY Robert L. Sthreiner College of Education University of Minnesota Minneapolis Reading and the Social Studies Friday , May 2 , L969 , 9:45 to 10:45 A LOGICAL ANALYSIS OF READING COMPREHENSION SKILLS IN THE SOCIAL STUDIES Introduction The following elements appear to be crucial in determtatng the degree of success students will have in obtaining meaning from sentences , paragraphs or passages : I. Students must have a certain degree of competency in determining what words mean . This work is a contribution to the formalization with the goal of understanding how competitive enterprises with heterogeneous organizations and belonging to different areas , can coexist in a same DBE . The formalization proposed has a logical reading based on processes models . Thanks to the use of the process models it is possible to organize some tools which allow to quickly build platforms for DBE management and governance and to make this flexible to comply with the different operating modes foreseen by the management of the enterprises which are part of the DBE The complex approach set by Jelena Mioc became a theory and was scientifically approved of in the 1960ies . The basic phases of the complex procedure are : preparation , final acquisition of letters and mastering reading separate words and short sentences ; developing a correct technique of reading and comprehending of the material to real and teaching initial writing ; developing logical reading . The postulations of the complex procedure resulted from the direct practice and were tested , proved and improved through practice , which contributed to their being well founded and reliable . This paper aims at presenting a comparative analysis of some relatively popular business process modelling techniques focusing on the associated software tools . The comparative framework proposed is based on the following major criteria : data flow capability , logical reasoning and understandability , specification of roles , and capability of simulation , flexibility and ease of use , simulation support and scope . However , the emphasis of this investigation is put on SME applicability of these tools and techniques . The personalization and intelligence of map service ultimately reflects in their symbol design . By establishing conceptual models of personalized map symbols , a theoretical discussion of the symbols is launched from a perspective which both human perceptions and logistic comprehension are considered in this paper , thus to help analyzing the relationship among geographic objects , map symbols and the interpretation of users and exploring the design basis of personalized map symbol . According to some key points such as relevant linguistic theories , the interpretation of the symbol contexts and symbolic statement syntax , design strategies of personalized map symbols is parsed . Until now , we have not yet had any MRC dataset for such a low-resource language as Vietnamese . In this paper , we introduce ViMMRC , a challenging machine comprehension corpus with multiple-choice questions , intended for research on the machine comprehension of Vietnamese text . This corpus includes 2,783 multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders . Deep learning promises to bring even better results but requires expensive and resource hungry hardware . In this paper , we demonstrate the use of deep learning in the context of machine comprehension by using non-GPU machines . Our results suggest that the good algorithm insight and detailed understanding of the dataset can help in getting meaningful results through deep learning even on non-GPU machines . This paper describes our system for SemEval-2018 Task 11 : Machine Comprehension using Commonsense Knowledge . We use Three-way Attentive Networks ( TriAN ) to model interactions between the passage , question and answers . Comprehending meaning from natural language is a primary objective of Natural Language Processing ( NLP ) , and text comprehension is the cornerstone for achieving this objective upon which all other problems like chat bots , language translation and others can be achieved . We report a Summary-Attentive Reader we designed to better emulate the human reading process , along with a dictiontary-based solution regarding out-of-vocabulary ( OOV ) words in the data , to generate answer based on machine comprehension of reading passages and question from the SQuAD benchmark . Our implementation of these features with two popular models ( Match LSTM and Dynamic Coattention ) was able to reach close to matching the results obtained from humans . Machine Reading and Comprehension recently has drawn a fair amount of attention in the field of natural language processing . In this paper , we consider integrating side information to improve machine comprehension on answering cloze-style questions more precisely . Machine reading and comprehension using differentiable reasoning models has recently been studied extensively , and memory networks have demonstrated promising performance on some reasoning tasks such as factual reasoning and basic deduction . However , as a natural language understanding model , memory networks still face challenges on the numeric representations for sentences , particularly the text representation method and the effectiveness of learned vector representations . For machine reading comprehension , the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy passages and getting ride of the noises is essential to improve its performance . Traditional attentive models attend to all words without explicit constraint , which results in inaccurate concentration on some dispensable words . Machine Reading Comprehension ( MRC ) is the task of answering a question over a paragraph of text . While neural MRC systems gain popularity and achieve noticeable performance , issues are being raised with the methodology used to establish their performance , particularly concerning the data design of gold standards that are used to evaluate them . This paper presents our recent work on the design and development of a new , large scale dataset , which we name MS MARCO , for MAchine Reading COmprehension . This new dataset is aimed to overcome a number of well-known weaknesses of previous publicly available datasets for the same task of reading comprehension and question answering . Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers , which are a long answer ( typically a paragraph ) and a short answer ( one or more entities inside the long answer ) . Despite the effectiveness of existing methods on this benchmark , they treat these two sub-tasks individually during training while ignoring their dependencies . The task includes many challenges , such as class imbalance , figurative expressions , context dependency , and long and complex sentences . This paper gives a detailed system description of our submission in SemEval 2019 Task 9 Subtask A. We transfer Self-Attention Network ( SAN ) , a successful model in machine reading comprehension field , into this task . Our model concentrates on modeling long-term dependency which is indispensable to parse long and complex sentences . This study aims at solving the Machine Reading Comprehension problem where questions have to be answered given a context passage . The challenge is to develop a computationally faster model which will have improved inference time . Nevertheless , how to model the complex interactions between the two different modalities is not an easy work . In contrast to struggling on multimodal feature fusion , in this paper , we propose to unify all the input information by natural language so as to convert VQA into a machine reading comprehension problem . With this transformation , our method not only can tackle VQA datasets that focus on observation based questions , but can also be naturally extended to handle knowledge-based VQA which requires to explore large-scale external knowledge base . The machine reading comprehension problem aims to extract crucial information from the given document to answer the relevant questions . Although many methods regarding the problem have been proposed , the similarity distraction problem inside remains unsolved . To evaluate the dataset , we implement several baseline systems based on pre-trained models , and the results show that the state-of-the-art model still underperforms human performance by a large margin . We hope the release of the dataset could further accelerate the machine reading comprehension research . Resources available : this https URL With the release of the dataset , we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension ( CMRC 2018 ) . We hope the release of the dataset could further accelerate the machine reading comprehension research in Chinese language . The data is available through : this https URL document : MEMEN : Multi - layer Embedding with Memory Networks for Machine Comprehension Machine comprehension ( MC ) There is no significant advantage in this as the frame - semantic approach used here does not possess the capability to generalise through a language model beyond exploiting one during the parsing phase . Thus , the key objective of evaluating machine comprehension abilities is maintained . Extracting entity - predicate triples — denoted as —from both the query and context document , we attempt to resolve queries using a number of rules with an increasing recall / precision trade - off as follows ( Table [ reference ] ) . Amongst all deep architectures , the three - layer LSTMN also performs best . We can study the memory activation mechanism of the machine reader by visualizing the attention scores . Figure [ reference ] shows four sentences sampled from the Penn Treebank validation set . LSTM [ reference ] showed its early potentials in NLP when a neural machine translation system that leverages LSTM source encoding gave highly competitive results compared to the best SMT models [ reference ] . LSTM encoders have since been explored for other tasks , including syntactic parsing [ reference ] , text classification [ reference ] and machine reading [ reference ] . Bidirectional extensions have become a standard configuration for achieving state - of - the - art accuracies among various tasks [ reference ][ reference ][ reference ] . SLSTMs are similar to BiLSTMs in their recurrent bi - directional message flow between words , but different in the design of state transition . 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 . Teaching machines to read , process , and comprehend natural language documents is a coveted goal for arti cial intelligence [ reference ][ reference ][ reference ] . Genuine reading comprehension is extremely challenging , since e ective comprehension involves thorough understanding of documents and sophisticated inference . Toward solving this machine reading comprehension problem , in recent years , several works have collected various datasets , in the form of question , passage , and answer , to test machine on answering a question based on the provided passage [ reference ][ reference ][ reference ][ reference ] . Some large - scale cloze - style datasets [ reference ][ reference ] have gained signi ca nt attention along with powerful deep learning models . Recent approaches on cloze - style datasets can be separated into two categories : single - turn and multi - turn reasoning . Recently Graph Neural Network ( GNN ) has been applied successfully to various NLP tasks that require reasoning , such as multi-hop machine reading comprehension . In this paper , we consider a novel case where reasoning is needed over graphs built from sequences , i.e. graph nodes with sequence data . Existing GNN models fulfill this goal by first summarizing the node sequences into fixed-dimensional vectors , then applying GNN on these vectors . To avoid information loss inherent in the early summarization and make sequential labeling tasks on GNN output feasible , we propose a new type of GNN called Graph Sequential Network ( GSN ) , which features a new message passing algorithm based on co-attention between a node and each of its neighbors . We validate the proposed GSN on two NLP tasks : interpretable multi-hop reading comprehension on HotpotQA and graph based fact verification on FEVER . Both tasks require reasoning over multiple documents or sentences . Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context . To achieve this , we propose an interpretable 3-module system called Explore-Propose-Assemble reader ( EPAr ) . Multi-hop reading comprehension focuses on one type of factoid question , where a system needs to properly integrate multiple pieces of evidence to correctly answer a question . Previous work approximates global evidence with local coreference information , encoding coreference chains with DAG-styled GRU layers within a gated-attention reader . Memory networks have been a popular choice among neural architectures for machine reading comprehension and question answering . While recent work revealed that memory networks ca n't truly perform multi-hop reasoning , we show in the present paper that vanilla memory networks are ineffective even in single-hop reading comprehension . We analyze the reasons for this on two cloze-style datasets , one from the medical domain and another including children 's fiction .
It is intepreted that teaching reading comprehension by using Cooperative Learning method was effective . Keywords : teaching , reading comprehension , Cooperative-Learning method
Reading comprehension ability is one of the most important and elementary abilities in the English study . However , the practi cal result is not so satisfied in the English reading classes of professional colleges .
This study used Kintsch ’s Construction Integration ( CI ) Model as a context for investigating reading comprehension assessment and its relationship to epistemic beliefs . Specifically , questions tied to levels of representation delineated in the CI model were used to investigate how individual differences may predict reading comprehension .
How to improve Reading Comprehension has become a theme of English teaching research . This article probes how to improve reading comprehension level from schema theory .
It is the basis of writing , translation , and also affects the students ' listening and speaking ability . Reading comprehension part always accounts for 40 % of the score in the English examination . However , at present , the college English reading course always can not meet the requirements of students ' good command of English reading ability .
So , reading is a valuable skill , this study will concentrate on this ability . This research is focused on the study of reading comprehension rate in the school “ Cesar Andrade Cordero ” in 10th grade students afternoon shift . This study is done to provide an analysis of the reading comprehension strategies implemented by the teacher of 10th grade .
Keyword : Numbered Head Together ( NHT ) , Reading Comprehension Reading is one skill that is very important in implementing teaching and learning process at Junior High School level . However , many students have difficulties in learning English , especially reading comprehension .
The purpose of this study was to investigate and evaluate the reading comprehension section of Iranian UEEs from 2004 to 2013 . So the corpus of the current study included the reading passages of all groups ( Math and physics , Natural science , Humanities , and Art ) and tried to evaluate and compare the following items of the passages : text domain , test rubric ( instruction ) , number of sentences , types of sentences , and types of reading comprehension questions .
The paper presents a corpus of text data and its corresponding gaze fixations obtained from autistic and non-autistic readers . The data was elicited through reading comprehension testing combined with eye-tracking recording . The corpus consists of 1034 content words tagged with their POS , syntactic role and three gaze-based measures corresponding to the autistic and control participants .
Fixation duration indicated that the closest advertisements slowed down information processing but there was no difference between the intermediate ( 40 pixel ) and far ( 80 pixel ) distance conditions . Our findings suggest that advertisements have a negative impact on users ' performance mostly when a lots of cognitive resources are required as for reading-for-comprehension .
However , Key - Value Memory Networks outperform all other methods on all three data source types . Reading from Wikipedia documents directly ( Doc ) outperforms an IE - based KB ( IE ) , which is an encouraging result towards automated machine reading though a gap to a human - annotated KB still remains ( 93.9 vs. 76.2 ) . The best memory representation for directly reading documents uses
Question answering has been a long - standing research problem . Recently , reading comprehension ( RC ) , a challenge to answer a question given textual evidence provided in a document set , has received much attention . Here , current mainstream studies have treated RC as a process of extracting an answer span from one passage RajpurkarZLL16 , RajpurkarJL18 or multiple passages JoshiCWZ17 , which is usually done by predicting the start and end positions of the answer
The theoretical framework of the proposed method is discussed in detail and two implementations are presented for the programming language ECMAScript ( JavaScript ) . The results from user testing with undergraduate students show that the proposed technique improves the student ’s learning outcomes in terms of syntax recall and logic comprehension , in comparison to traditional source code editors .
, ( BT ) U.S. DEPAR TPAIMT OF HEALTH . EDUCATION & WELFARE OFFICE OF EDUCATION THIS DOCUMENT HAS BEEN REPRODUCED EXACTLY AS RECEIVED FROMTHE PERSON OR ORGANIZATION ORIGINATING IT POINTS OF VIEW OR OPINIONS STATED DO NOT NECES SARILY PEPRESENT OFFICIAL OFFICE OF EDUCATION POSITION OR POLICY Robert L. Sthreiner College of Education University of Minnesota Minneapolis Reading and the Social Studies Friday , May 2 , L969 , 9:45 to 10:45 A LOGICAL ANALYSIS OF READING COMPREHENSION SKILLS IN THE SOCIAL STUDIES Introduction The following elements appear to be crucial in determtatng the degree of success students will have in obtaining meaning from sentences , paragraphs or passages : I. Students must have a certain degree of competency in determining what words mean .
This work is a contribution to the formalization with the goal of understanding how competitive enterprises with heterogeneous organizations and belonging to different areas , can coexist in a same DBE . The formalization proposed has a logical reading based on processes models . Thanks to the use of the process models it is possible to organize some tools which allow to quickly build platforms for DBE management and governance and to make this flexible to comply with the different operating modes foreseen by the management of the enterprises which are part of the DBE
The complex approach set by Jelena Mioc became a theory and was scientifically approved of in the 1960ies . The basic phases of the complex procedure are : preparation , final acquisition of letters and mastering reading separate words and short sentences ; developing a correct technique of reading and comprehending of the material to real and teaching initial writing ; developing logical reading . The postulations of the complex procedure resulted from the direct practice and were tested , proved and improved through practice , which contributed to their being well founded and reliable .
This paper aims at presenting a comparative analysis of some relatively popular business process modelling techniques focusing on the associated software tools . The comparative framework proposed is based on the following major criteria : data flow capability , logical reasoning and understandability , specification of roles , and capability of simulation , flexibility and ease of use , simulation support and scope . However , the emphasis of this investigation is put on SME applicability of these tools and techniques .
The personalization and intelligence of map service ultimately reflects in their symbol design . By establishing conceptual models of personalized map symbols , a theoretical discussion of the symbols is launched from a perspective which both human perceptions and logistic comprehension are considered in this paper , thus to help analyzing the relationship among geographic objects , map symbols and the interpretation of users and exploring the design basis of personalized map symbol . According to some key points such as relevant linguistic theories , the interpretation of the symbol contexts and symbolic statement syntax , design strategies of personalized map symbols is parsed .
Until now , we have not yet had any MRC dataset for such a low-resource language as Vietnamese . In this paper , we introduce ViMMRC , a challenging machine comprehension corpus with multiple-choice questions , intended for research on the machine comprehension of Vietnamese text . This corpus includes 2,783 multiple-choice questions and answers based on a set of 417 Vietnamese texts used for teaching reading comprehension for 1st to 5th graders .
Deep learning promises to bring even better results but requires expensive and resource hungry hardware . In this paper , we demonstrate the use of deep learning in the context of machine comprehension by using non-GPU machines . Our results suggest that the good algorithm insight and detailed understanding of the dataset can help in getting meaningful results through deep learning even on non-GPU machines .
This paper describes our system for SemEval-2018 Task 11 : Machine Comprehension using Commonsense Knowledge . We use Three-way Attentive Networks ( TriAN ) to model interactions between the passage , question and answers .
Comprehending meaning from natural language is a primary objective of Natural Language Processing ( NLP ) , and text comprehension is the cornerstone for achieving this objective upon which all other problems like chat bots , language translation and others can be achieved . We report a Summary-Attentive Reader we designed to better emulate the human reading process , along with a dictiontary-based solution regarding out-of-vocabulary ( OOV ) words in the data , to generate answer based on machine comprehension of reading passages and question from the SQuAD benchmark . Our implementation of these features with two popular models ( Match LSTM and Dynamic Coattention ) was able to reach close to matching the results obtained from humans .
Machine Reading and Comprehension recently has drawn a fair amount of attention in the field of natural language processing . In this paper , we consider integrating side information to improve machine comprehension on answering cloze-style questions more precisely .
Machine reading and comprehension using differentiable reasoning models has recently been studied extensively , and memory networks have demonstrated promising performance on some reasoning tasks such as factual reasoning and basic deduction . However , as a natural language understanding model , memory networks still face challenges on the numeric representations for sentences , particularly the text representation method and the effectiveness of learned vector representations .
For machine reading comprehension , the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy passages and getting ride of the noises is essential to improve its performance . Traditional attentive models attend to all words without explicit constraint , which results in inaccurate concentration on some dispensable words .
Machine Reading Comprehension ( MRC ) is the task of answering a question over a paragraph of text . While neural MRC systems gain popularity and achieve noticeable performance , issues are being raised with the methodology used to establish their performance , particularly concerning the data design of gold standards that are used to evaluate them .
This paper presents our recent work on the design and development of a new , large scale dataset , which we name MS MARCO , for MAchine Reading COmprehension . This new dataset is aimed to overcome a number of well-known weaknesses of previous publicly available datasets for the same task of reading comprehension and question answering .
Natural Questions is a new challenging machine reading comprehension benchmark with two-grained answers , which are a long answer ( typically a paragraph ) and a short answer ( one or more entities inside the long answer ) . Despite the effectiveness of existing methods on this benchmark , they treat these two sub-tasks individually during training while ignoring their dependencies .
The task includes many challenges , such as class imbalance , figurative expressions , context dependency , and long and complex sentences . This paper gives a detailed system description of our submission in SemEval 2019 Task 9 Subtask A. We transfer Self-Attention Network ( SAN ) , a successful model in machine reading comprehension field , into this task . Our model concentrates on modeling long-term dependency which is indispensable to parse long and complex sentences .
This study aims at solving the Machine Reading Comprehension problem where questions have to be answered given a context passage . The challenge is to develop a computationally faster model which will have improved inference time .
Nevertheless , how to model the complex interactions between the two different modalities is not an easy work . In contrast to struggling on multimodal feature fusion , in this paper , we propose to unify all the input information by natural language so as to convert VQA into a machine reading comprehension problem . With this transformation , our method not only can tackle VQA datasets that focus on observation based questions , but can also be naturally extended to handle knowledge-based VQA which requires to explore large-scale external knowledge base .
The machine reading comprehension problem aims to extract crucial information from the given document to answer the relevant questions . Although many methods regarding the problem have been proposed , the similarity distraction problem inside remains unsolved .
To evaluate the dataset , we implement several baseline systems based on pre-trained models , and the results show that the state-of-the-art model still underperforms human performance by a large margin . We hope the release of the dataset could further accelerate the machine reading comprehension research . Resources available : this https URL
With the release of the dataset , we hosted the Second Evaluation Workshop on Chinese Machine Reading Comprehension ( CMRC 2018 ) . We hope the release of the dataset could further accelerate the machine reading comprehension research in Chinese language . The data is available through : this https URL
document : MEMEN : Multi - layer Embedding with Memory Networks for Machine Comprehension Machine comprehension ( MC )
There is no significant advantage in this as the frame - semantic approach used here does not possess the capability to generalise through a language model beyond exploiting one during the parsing phase . Thus , the key objective of evaluating machine comprehension abilities is maintained . Extracting entity - predicate triples — denoted as —from both the query and context document , we attempt to resolve queries using a number of rules with an increasing recall / precision trade - off as follows ( Table [ reference ] ) .
Amongst all deep architectures , the three - layer LSTMN also performs best . We can study the memory activation mechanism of the machine reader by visualizing the attention scores . Figure [ reference ] shows four sentences sampled from the Penn Treebank validation set .
LSTM [ reference ] showed its early potentials in NLP when a neural machine translation system that leverages LSTM source encoding gave highly competitive results compared to the best SMT models [ reference ] . LSTM encoders have since been explored for other tasks , including syntactic parsing [ reference ] , text classification [ reference ] and machine reading [ reference ] . Bidirectional extensions have become a standard configuration for achieving state - of - the - art accuracies among various tasks [ reference ][ reference ][ reference ] . SLSTMs are similar to BiLSTMs in their recurrent bi - directional message flow between words , but different in the design of state transition .
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 .
Teaching machines to read , process , and comprehend natural language documents is a coveted goal for arti cial intelligence [ reference ][ reference ][ reference ] . Genuine reading comprehension is extremely challenging , since e ective comprehension involves thorough understanding of documents and sophisticated inference . Toward solving this machine reading comprehension problem , in recent years , several works have collected various datasets , in the form of question , passage , and answer , to test machine on answering a question based on the provided passage [ reference ][ reference ][ reference ][ reference ] . Some large - scale cloze - style datasets [ reference ][ reference ] have gained signi ca nt attention along with powerful deep learning models . Recent approaches on cloze - style datasets can be separated into two categories : single - turn and multi - turn reasoning .
Recently Graph Neural Network ( GNN ) has been applied successfully to various NLP tasks that require reasoning , such as multi-hop machine reading comprehension . In this paper , we consider a novel case where reasoning is needed over graphs built from sequences , i.e. graph nodes with sequence data . Existing GNN models fulfill this goal by first summarizing the node sequences into fixed-dimensional vectors , then applying GNN on these vectors . To avoid information loss inherent in the early summarization and make sequential labeling tasks on GNN output feasible , we propose a new type of GNN called Graph Sequential Network ( GSN ) , which features a new message passing algorithm based on co-attention between a node and each of its neighbors . We validate the proposed GSN on two NLP tasks : interpretable multi-hop reading comprehension on HotpotQA and graph based fact verification on FEVER . Both tasks require reasoning over multiple documents or sentences .
Multi-hop reading comprehension requires the model to explore and connect relevant information from multiple sentences/documents in order to answer the question about the context . To achieve this , we propose an interpretable 3-module system called Explore-Propose-Assemble reader ( EPAr ) .
Multi-hop reading comprehension focuses on one type of factoid question , where a system needs to properly integrate multiple pieces of evidence to correctly answer a question . Previous work approximates global evidence with local coreference information , encoding coreference chains with DAG-styled GRU layers within a gated-attention reader .
Memory networks have been a popular choice among neural architectures for machine reading comprehension and question answering . While recent work revealed that memory networks ca n't truly perform multi-hop reasoning , we show in the present paper that vanilla memory networks are ineffective even in single-hop reading comprehension . We analyze the reasons for this on two cloze-style datasets , one from the medical domain and another including children 's fiction .
TEACHING READING COMPREHENSION BY USING COOPERATIVE LEARNING METHOD TO THE FOURTH SEMESTER STUDENTS OF ENGLISH EDUCATION STUDY PROGRAM FKIP UNIVERSITAS MUHAMMADIYAH PALEMBANG An Exploration to English Reading Teaching Innovtion in Professional Colleges Reading comprehension assessments: Effect of epistemic beliefs on text availability and question type Schemata Theory and Its Application in English Reading Research on English Reading Teaching Based on CBI Determination of the teaching methodology applied for reading comprehension development of 10th graders at "Cesar Andrade Cordero School". THE EFFECTIVENESS OF USING NUMBERED HEAD TOGETHER (NHT) TO INCREASE STUDENTS READING COMPREHENSION OF SEVENTH GRADE AT MTS ASWAJA TUNGGANGRI A Comparative Analysis of Reading Comprehension Tests of Iranian Universities’ Entrance Examinations (UEE) A Corpus of Text Data and Gaze Fixations from Autistic and Non-Autistic Adults Online advertisement: how are visual strategies affected by the distance and the animation of banners? Key-Value Memory Networks for Directly Reading Documents Multi-style Generative Reading Comprehension Assessing the Effectiveness of Emoticon-Like Scripting in Computer Programming A Logical Analysis of Reading Comprehension Skills in the Social Studies. An Approach to Digital Business Ecosystems based on Process Models Application of the complex method in teaching initial reading and writing in the first form of elementary school with special regard on the individualization of teaching Towards a Thorough Evaluation Framework of Software Tools Suitable for Small and Medium Size Enterprises Focusing on Modelling and Simulating Business Processes Personalized Map Symbol Design Mechanism Based on Linguistics A Pilot Study on Multiple Choice Machine Reading Comprehension for Vietnamese Texts Towards Achieving Machine Comprehension Using Deep Learning on Non-GPU Machines Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension CAESAR: Context Awareness Enabled Summary-Attentive Reader Integrating Side Information for Boosting Machine Comprehension Convolutional End-to-End Memory Networks for Multi-Hop Reasoning SG-Net: Syntax-Guided Machine Reading Comprehension A Framework for Evaluation of Machine Reading Comprehension Gold Standards MS MARCO: A Human Generated MAchine Reading COmprehension Dataset Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches Making Neural Machine Reading Comprehension Faster Visual Question Answering as Reading Comprehension Named Entity Filters for Robust Machine Reading Comprehension A Sentence Cloze Dataset for Chinese Machine Reading Comprehension A Span-Extraction Dataset for Chinese Machine Reading Comprehension MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension Teaching Machines to Read and Comprehend Long Short-Term Memory-Networks for Machine Reading Sentence-State LSTM for Text Representation Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification ReasoNet: Learning to Stop Reading in Machine Comprehension Graph Sequential Network for Reasoning over Sequences Explore, Propose, and Assemble: An Interpretable Model for Multi-Hop Reading Comprehension Exploring Graph-structured Passage Representation for Multi-hop Reading Comprehension with Graph Neural Networks Why can't memory networks read effectively?
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LREC EMNLP ACL BMSD CIKM CoCo@NIPS SemEval@NAACL-HLT TAAI EMNLP/IJCNLP (1) ArXiv NIPS EMNLP ACL ACL CoCo@NIPS ACL (1)
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