<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wu-etal-2024-synthesizing">
    <titleInfo>
        <title>Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Fanyou</namePart>
        <namePart type="family">Wu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Weijie</namePart>
        <namePart type="family">Xu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chandan</namePart>
        <namePart type="family">Reddy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Srinivasan</namePart>
        <namePart type="family">Sengamedu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Findings of the Association for Computational Linguistics ACL 2024</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Lun-Wei</namePart>
            <namePart type="family">Ku</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Andre</namePart>
            <namePart type="family">Martins</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vivek</namePart>
            <namePart type="family">Srikumar</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Bangkok, Thailand and virtual meeting</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this study, we tackle the challenge of inadequate and costly training data that has hindered the development of conversational question answering (ConvQA) systems. Enterprises have a large corpus of diverse internal documents. Instead of relying on a searching engine, a more compelling approach for people to comprehend these documents is to create a dialogue system. In this paper, we propose a robust dialog synthesising method. We learn the segmentation of data for the dialog task instead of using segmenting at sentence boundaries. The synthetic dataset generated by our proposed method achieves superior quality when compared to WikiDialog, as assessed through machine and human evaluations. By employing our inpainted data for ConvQA retrieval system pre-training, we observed a notable improvement in performance across OR-QuAC benchmarks.</abstract>
    <identifier type="citekey">wu-etal-2024-synthesizing</identifier>
    <location>
        <url>https://aclanthology.org/2024.findings-acl.477</url>
    </location>
    <part>
        <date>2024-08</date>
        <extent unit="page">
            <start>8012</start>
            <end>8026</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation
%A Wu, Fanyou
%A Xu, Weijie
%A Reddy, Chandan
%A Sengamedu, Srinivasan
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F wu-etal-2024-synthesizing
%X In this study, we tackle the challenge of inadequate and costly training data that has hindered the development of conversational question answering (ConvQA) systems. Enterprises have a large corpus of diverse internal documents. Instead of relying on a searching engine, a more compelling approach for people to comprehend these documents is to create a dialogue system. In this paper, we propose a robust dialog synthesising method. We learn the segmentation of data for the dialog task instead of using segmenting at sentence boundaries. The synthetic dataset generated by our proposed method achieves superior quality when compared to WikiDialog, as assessed through machine and human evaluations. By employing our inpainted data for ConvQA retrieval system pre-training, we observed a notable improvement in performance across OR-QuAC benchmarks.
%U https://aclanthology.org/2024.findings-acl.477
%P 8012-8026

Markdown (Informal)

[Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation](https://aclanthology.org/2024.findings-acl.477) (Wu et al., Findings 2024)

• Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation (Wu et al., Findings 2024)

ACL

• Fanyou Wu, Weijie Xu, Chandan Reddy, and Srinivasan Sengamedu. 2024. Synthesizing Conversations from Unlabeled Documents using Automatic Response Segmentation. In Findings of the Association for Computational Linguistics ACL 2024, pages 8012–8026, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.