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Making Neural QA as Simple as Possible but not Simpler
2017
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)
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Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-toend neural architectures for QA. ...
We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, a composition function ...
Acknowledgments We thank Sebastian Riedel, Philippe Thomas, Leonhard Hennig and Omer Levy for comments on an early draft of this work as well as the anonymous reviewers for their insightful comments. ...
doi:10.18653/v1/k17-1028
dblp:conf/conll/WeissenbornWS17
fatcat:wg6y55lkfvcm7j5rxthcwss24u
Making Neural QA as Simple as Possible but not Simpler
[article]
2017
arXiv
pre-print
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Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. ...
We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, a composition function ...
Acknowledgments We thank Sebastian Riedel, Philippe Thomas, Leonhard Hennig and Omer Levy for comments on an early draft of this work as well as the anonymous reviewers for their insightful comments. ...
arXiv:1703.04816v3
fatcat:d46qtwxovjf2znm7kri553m63e
Question Answering by Reasoning Across Documents with Graph Convolutional Networks
[article]
2019
arXiv
pre-print
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Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset, WikiHop (Welbl et al., 2018). ...
We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. ...
This project is supported by SAP Innovation Center Network, ERC Starting Grant BroadSem (678254) and the Dutch Organization for Scientific Research (NWO) VIDI 639.022.518. ...
arXiv:1808.09920v3
fatcat:pdd4mc5tkjgezeduqouky2yrba
Question Answering by Reasoning Across Documents with Graph Convolutional Networks
2019
Proceedings of the 2019 Conference of the North
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Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset, WIKIHOP (Welbl et al., 2018). ...
We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. ...
These systems, given a text and a question, need to answer the query relying on the given document. ...
doi:10.18653/v1/n19-1240
dblp:conf/naacl/CaoAT19
fatcat:7jdqxkfhgnetdcc5mjtlmji4xy
Jack the Reader – A Machine Reading Framework
2018
Proceedings of ACL 2018, System Demonstrations
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For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions. ...
Providing a set of useful primitives operating in a single framework of related tasks would allow for expressive modelling, and easier model comparison and replication. ...
The architecture is built by a sequence of modular neural building blocks, in short modules. ...
doi:10.18653/v1/p18-4005
dblp:conf/acl/WeissenbornMAWR18
fatcat:or5rhfs3grfsvkulm3kkatddq4
Jack the Reader - A Machine Reading Framework
[article]
2018
arXiv
pre-print
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For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions. ...
Providing a set of useful primitives operating in a single framework of related tasks would allow for expressive modelling, and easier model comparison and replication. ...
The architecture is built by a sequence of modular neural building blocks, in short modules. ...
arXiv:1806.08727v1
fatcat:p4r52au3zzewvaop66sy4lbyxu
End-To-End Neural Network for Paraphrased Question Answering Architecture with Single Supporting Line in Bangla Language
2020
International Journal of Future Computer and Communication
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To predict appropriate answer, model is trained with question-answer pair and a supporting line. ...
miniature sub tasks to accomplish a whole AI-system having capability of answering and reasoning complicated and long questions through understating paragraph. ...
Another paper [4] where researchers proposed an efficient neural model denoted as FastQA for question answering which outperforms existing model over very popular recent datasets named SQuAD, [18] ...
doi:10.18178/ijfcc.2020.9.3.565
fatcat:gp726p3afnczboj6ihgmpzhone
Densely Connected Attention Propagation for Reading Comprehension
[article]
2019
arXiv
pre-print
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We propose DecaProp (Densely Connected Attention Propagation), a new densely connected neural architecture for reading comprehension (RC). There are two distinct characteristics of our model. ...
To this end, we propose novel Bidirectional Attention Connectors (BAC) for efficiently forging connections throughout the network. We conduct extensive experiments on four challenging RC benchmarks. ...
The authors would like to thank the anonymous reviewers of NeuRIPS 2018 for their valuable time and feedback! ...
arXiv:1811.04210v2
fatcat:mysf6uphfrc67fftflcpp2v62u
A3Net:Adversarial-and-Attention Network for Machine Reading Comprehension
[chapter]
2018
Lecture Notes in Computer Science
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Second, we propose a multi-layer attention network utilizing three kinds of high-efficiency attention mechanisms. ...
Multi-layer attention conducts interaction between question and passage within each layer, which contributes to reasonable representation and understanding of the model. ...
For example, by randomly dropping units, dropout is widely used as a simple way to prevent neural networks from overfitting. ...
doi:10.1007/978-3-319-99495-6_6
fatcat:gw6btg53e5dchijyjtoww6gwfe
A Study of the Tasks and Models in Machine Reading Comprehension
[article]
2020
arXiv
pre-print
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and complex-reasoning MRC tasks; 2) the architecture designs, attention mechanisms, and performance-boosting approaches for developing neural-network-based MRC models; 3) some recently proposed transfer ...
To provide a survey on the existing tasks and models in Machine Reading Comprehension (MRC), this report reviews: 1) the dataset collection and performance evaluation of some representative simple-reasoning ...
Architecture Designs for MRC Models This section introduces some representative architecture designs for MRC models, which cover both simple-reasoning and complex-reasoning MRC models. ...
arXiv:2001.08635v1
fatcat:yuc3fx4jjvhkxnbv4o6kerunve
Efficient and Robust Question Answering from Minimal Context over Documents
2018
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Neural models for question answering (QA) over documents have achieved significant performance improvements. ...
In this paper, we study the minimal context required to answer the question, and find that most questions in existing datasets can be answered with a small set of sentences. ...
First, we studied the minimal context required to answer the question in existing datasets and found that most questions can be answered using a small set of sentences. ...
doi:10.18653/v1/p18-1160
dblp:conf/acl/SocherZXM18
fatcat:7fme4rqlmjgpngy5pdhxoxpg6u
New Vietnamese Corpus for Machine Reading Comprehension of Health News Articles
2022
ACM Transactions on Asian and Low-Resource Language Information Processing
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We introduce a process for creating a high-quality corpus for the Vietnamese machine reading comprehension task. Linguistically, our corpus accommodates diversity in question and answer types. ...
Machine reading comprehension is a natural language understanding task where the computing system is required to read a text and then find the answer to a specific question posed by a human. ...
Also, we thank to Sang Thanh Tran and the annotator team for creating and revising the question-answer data. ...
doi:10.1145/3527631
fatcat:mgnypmkaavce7k6hyp4j7c5ce4
SubjQA: A Dataset for Subjectivity and Review Comprehension
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2004.14283v2 fatcat:vsxyoukbangbhajesrohdvg2feOther Versions
For instance, a subjective question may or may not be associated with a subjective answer. ...
We release an English QA dataset (SubjQA) based on customer reviews, containing subjectivity annotations for questions and answer spans across 6 distinct domains. ...
Acknowledgements We are grateful to the Nordic Language Processing Laboratory (NLPL) for providing access to its supercluster infrastructure, and the anonymous reviewers for their helpful feedback. ...
arXiv:2004.14283v3
fatcat:szkah5oscbcd7mrlmk3pougadm
Efficient and Robust Question Answering from Minimal Context over Documents
[article]
2018
arXiv
pre-print
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Neural models for question answering (QA) over documents have achieved significant performance improvements. ...
In this paper, we study the minimal context required to answer the question, and find that most questions in existing datasets can be answered with a small set of sentences. ...
Acknowledgments We thank the anonymous reviewers and the Salesforce Research team members for their thoughtful comments and discussions. ...
arXiv:1805.08092v1
fatcat:cgswcf2slzfifgkrtazhxegw3y
SRQA: Synthetic Reader for Factoid Question Answering
2019
Knowledge-Based Systems
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2020 pre-print arXiv:2009.01630v1 fatcat:pjvzd6qa6vaghlvbwhphgrsqwm
We introduce a new model called SRQA, which means Synthetic Reader for Factoid Question Answering. ...
The question answering system can answer questions from various fields and forms with deep neural networks, but it still lacks effective ways when facing multiple evidences. ...
For example, by randomly dropping units, dropout [37] is widely used as a simple way to prevent neural networks from overfitting. ...
doi:10.1016/j.knosys.2019.105415
fatcat:agstiyiqgbeobdocsclhdtcqtu
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