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        <title>Relation Module for Non-Answerable Predictions on Reading Comprehension</title>
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        <namePart type="given">Kevin</namePart>
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    <name type="personal">
        <namePart type="given">Xiaodong</namePart>
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    <name type="personal">
        <namePart type="given">Bowen</namePart>
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            <title>Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)</title>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Machine reading comprehension (MRC) has attracted significant amounts of research attention recently, due to an increase of challenging reading comprehension datasets. In this paper, we aim to improve a MRC model’s ability to determine whether a question has an answer in a given context (e.g. the recently proposed SQuAD 2.0 task). The relation module consists of both semantic extraction and relational information. We first extract high level semantics as objects from both question and context with multi-head self-attentive pooling. These semantic objects are then passed to a relation network, which generates relationship scores for each object pair in a sentence. These scores are used to determine whether a question is non-answerable. We test the relation module on the SQuAD 2.0 dataset using both the BiDAF and BERT models as baseline readers. We obtain 1.8% gain of F1 accuracy on top of the BiDAF reader, and 1.0% on top of the BERT base model. These results show the effectiveness of our relation module on MRC.</abstract>
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    <identifier type="doi">10.18653/v1/K19-1070</identifier>
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%0 Conference Proceedings
%T Relation Module for Non-Answerable Predictions on Reading Comprehension
%A Huang, Kevin
%A Tang, Yun
%A Huang, Jing
%A He, Xiaodong
%A Zhou, Bowen
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F huang-etal-2019-relation
%X Machine reading comprehension (MRC) has attracted significant amounts of research attention recently, due to an increase of challenging reading comprehension datasets. In this paper, we aim to improve a MRC model’s ability to determine whether a question has an answer in a given context (e.g. the recently proposed SQuAD 2.0 task). The relation module consists of both semantic extraction and relational information. We first extract high level semantics as objects from both question and context with multi-head self-attentive pooling. These semantic objects are then passed to a relation network, which generates relationship scores for each object pair in a sentence. These scores are used to determine whether a question is non-answerable. We test the relation module on the SQuAD 2.0 dataset using both the BiDAF and BERT models as baseline readers. We obtain 1.8% gain of F1 accuracy on top of the BiDAF reader, and 1.0% on top of the BERT base model. These results show the effectiveness of our relation module on MRC.
%R 10.18653/v1/K19-1070
%U https://aclanthology.org/K19-1070
%U https://doi.org/10.18653/v1/K19-1070
%P 747-756

Markdown (Informal)

[Relation Module for Non-Answerable Predictions on Reading Comprehension](https://aclanthology.org/K19-1070) (Huang et al., CoNLL 2019)

• Relation Module for Non-Answerable Predictions on Reading Comprehension (Huang et al., CoNLL 2019)

ACL

• Kevin Huang, Yun Tang, Jing Huang, Xiaodong He, and Bowen Zhou. 2019. Relation Module for Non-Answerable Predictions on Reading Comprehension. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 747–756, Hong Kong, China. Association for Computational Linguistics.