<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-yang-2023-generating">
    <titleInfo>
        <title>Generating Extractive Answers: Gated Recurrent Memory Reader for Conversational Question Answering</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Xuanyu</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Qing</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics: EMNLP 2023</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Juan</namePart>
            <namePart type="family">Pino</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Singapore</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Conversational question answering (CQA) is a more complicated task than traditional single-turn machine reading comprehension (MRC). Different from large language models (LLMs) like ChatGPT, the models of CQA need to extract answers from given contents to answer follow-up questions according to conversation history. In this paper, we propose a novel architecture, i.e., Gated Recurrent Memory Reader (GRMR), which integrates traditional extractive MRC models into a generalized sequence-to-sequence framework. After the passage is encoded, the decoder will generate the extractive answers turn by turn. Different from previous models that concatenate the previous questions and answers as context superficially and redundantly, our model can use less storage space and consider historical memory deeply and selectively. Experiments on the Conversational Question Answering (CoQA) dataset show that our model achieves comparable results to most models with the least space occupancy.</abstract>
    <identifier type="citekey">zhang-yang-2023-generating</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.516</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.516</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>7699</start>
            <end>7704</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Generating Extractive Answers: Gated Recurrent Memory Reader for Conversational Question Answering
%A Zhang, Xuanyu
%A Yang, Qing
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zhang-yang-2023-generating
%X Conversational question answering (CQA) is a more complicated task than traditional single-turn machine reading comprehension (MRC). Different from large language models (LLMs) like ChatGPT, the models of CQA need to extract answers from given contents to answer follow-up questions according to conversation history. In this paper, we propose a novel architecture, i.e., Gated Recurrent Memory Reader (GRMR), which integrates traditional extractive MRC models into a generalized sequence-to-sequence framework. After the passage is encoded, the decoder will generate the extractive answers turn by turn. Different from previous models that concatenate the previous questions and answers as context superficially and redundantly, our model can use less storage space and consider historical memory deeply and selectively. Experiments on the Conversational Question Answering (CoQA) dataset show that our model achieves comparable results to most models with the least space occupancy.
%R 10.18653/v1/2023.findings-emnlp.516
%U https://aclanthology.org/2023.findings-emnlp.516
%U https://doi.org/10.18653/v1/2023.findings-emnlp.516
%P 7699-7704

Markdown (Informal)

[Generating Extractive Answers: Gated Recurrent Memory Reader for Conversational Question Answering](https://aclanthology.org/2023.findings-emnlp.516) (Zhang & Yang, Findings 2023)

• Generating Extractive Answers: Gated Recurrent Memory Reader for Conversational Question Answering (Zhang & Yang, Findings 2023)

ACL

• Xuanyu Zhang and Qing Yang. 2023. Generating Extractive Answers: Gated Recurrent Memory Reader for Conversational Question Answering. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7699–7704, Singapore. Association for Computational Linguistics.