<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tsunomori-etal-2023-time">
    <titleInfo>
        <title>Time-Considerable Dialogue Models via Reranking by Time Dependency</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yuiko</namePart>
        <namePart type="family">Tsunomori</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Masakazu</namePart>
        <namePart type="family">Ishihata</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hiroaki</namePart>
        <namePart type="family">Sugiyama</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>In the last few years, generative dialogue models have shown excellent performance and have been used for various applications. As chatbots become more prevalent in our daily lives, more and more people expect them to behave more like humans, but existing dialogue models do not consider the time information that people are constantly aware of. In this paper, we aim to construct a time-considerable dialogue model that actively utilizes time information. First, we categorize responses by their naturalness at different times and introduce a new metric to classify responses into our categories. Then, we propose a new reranking method to make the existing dialogue model time-considerable using the proposed metric and subjectively evaluate the performances of the obtained time-considerable dialogue models by humans.</abstract>
    <identifier type="citekey">tsunomori-etal-2023-time</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-emnlp.341</identifier>
    <location>
        <url>https://aclanthology.org/2023.findings-emnlp.341/</url>
    </location>
    <part>
        <date>2023-12</date>
        <extent unit="page">
            <start>5136</start>
            <end>5149</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Time-Considerable Dialogue Models via Reranking by Time Dependency
%A Tsunomori, Yuiko
%A Ishihata, Masakazu
%A Sugiyama, Hiroaki
%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 tsunomori-etal-2023-time
%X In the last few years, generative dialogue models have shown excellent performance and have been used for various applications. As chatbots become more prevalent in our daily lives, more and more people expect them to behave more like humans, but existing dialogue models do not consider the time information that people are constantly aware of. In this paper, we aim to construct a time-considerable dialogue model that actively utilizes time information. First, we categorize responses by their naturalness at different times and introduce a new metric to classify responses into our categories. Then, we propose a new reranking method to make the existing dialogue model time-considerable using the proposed metric and subjectively evaluate the performances of the obtained time-considerable dialogue models by humans.
%R 10.18653/v1/2023.findings-emnlp.341
%U https://aclanthology.org/2023.findings-emnlp.341/
%U https://doi.org/10.18653/v1/2023.findings-emnlp.341
%P 5136-5149

Markdown (Informal)

[Time-Considerable Dialogue Models via Reranking by Time Dependency](https://aclanthology.org/2023.findings-emnlp.341/) (Tsunomori et al., Findings 2023)

• Time-Considerable Dialogue Models via Reranking by Time Dependency (Tsunomori et al., Findings 2023)

ACL

• Yuiko Tsunomori, Masakazu Ishihata, and Hiroaki Sugiyama. 2023. Time-Considerable Dialogue Models via Reranking by Time Dependency. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5136–5149, Singapore. Association for Computational Linguistics.