<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chan-fan-2019-recurrent">
    <titleInfo>
        <title>A Recurrent BERT-based Model for Question Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ying-Hong</namePart>
        <namePart type="family">Chan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yao-Chung</namePart>
        <namePart type="family">Fan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2nd Workshop on Machine Reading for Question Answering</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Adam</namePart>
            <namePart type="family">Fisch</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Alon</namePart>
            <namePart type="family">Talmor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Robin</namePart>
            <namePart type="family">Jia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Minjoon</namePart>
            <namePart type="family">Seo</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Danqi</namePart>
            <namePart type="family">Chen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Hong Kong, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks. We introduce three neural architectures built on top of BERT for question generation tasks. The first one is a straightforward BERT employment, which reveals the defects of directly using BERT for text generation. Accordingly, we propose another two models by restructuring our BERT employment into a sequential manner for taking information from previous decoded results. Our models are trained and evaluated on the recent question-answering dataset SQuAD. Experiment results show that our best model yields state-of-the-art performance which advances the BLEU 4 score of the existing best models from 16.85 to 22.17.</abstract>
    <identifier type="citekey">chan-fan-2019-recurrent</identifier>
    <identifier type="doi">10.18653/v1/D19-5821</identifier>
    <location>
        <url>https://aclanthology.org/D19-5821</url>
    </location>
    <part>
        <date>2019-11</date>
        <extent unit="page">
            <start>154</start>
            <end>162</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Recurrent BERT-based Model for Question Generation
%A Chan, Ying-Hong
%A Fan, Yao-Chung
%Y Fisch, Adam
%Y Talmor, Alon
%Y Jia, Robin
%Y Seo, Minjoon
%Y Choi, Eunsol
%Y Chen, Danqi
%S Proceedings of the 2nd Workshop on Machine Reading for Question Answering
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F chan-fan-2019-recurrent
%X In this study, we investigate the employment of the pre-trained BERT language model to tackle question generation tasks. We introduce three neural architectures built on top of BERT for question generation tasks. The first one is a straightforward BERT employment, which reveals the defects of directly using BERT for text generation. Accordingly, we propose another two models by restructuring our BERT employment into a sequential manner for taking information from previous decoded results. Our models are trained and evaluated on the recent question-answering dataset SQuAD. Experiment results show that our best model yields state-of-the-art performance which advances the BLEU 4 score of the existing best models from 16.85 to 22.17.
%R 10.18653/v1/D19-5821
%U https://aclanthology.org/D19-5821
%U https://doi.org/10.18653/v1/D19-5821
%P 154-162

Markdown (Informal)

[A Recurrent BERT-based Model for Question Generation](https://aclanthology.org/D19-5821) (Chan & Fan, 2019)

• A Recurrent BERT-based Model for Question Generation (Chan & Fan, 2019)

ACL

• Ying-Hong Chan and Yao-Chung Fan. 2019. A Recurrent BERT-based Model for Question Generation. In Proceedings of the 2nd Workshop on Machine Reading for Question Answering, pages 154–162, Hong Kong, China. Association for Computational Linguistics.