<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="chernyavskiy-etal-2024-groundhog">
    <titleInfo>
        <title>GroundHog: Dialogue Generation using Multi-Grained Linguistic Input</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Alexander</namePart>
        <namePart type="family">Chernyavskiy</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lidiia</namePart>
        <namePart type="family">Ostyakova</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dmitry</namePart>
        <namePart type="family">Ilvovsky</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-03</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Strube</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chloe</namePart>
            <namePart type="family">Braud</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christian</namePart>
            <namePart type="family">Hardmeier</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Junyi</namePart>
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sharid</namePart>
            <namePart type="family">Loaiciga</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Amir</namePart>
            <namePart type="family">Zeldes</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Chuyuan</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">St. Julians, Malta</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Recent language models have significantly boosted conversational AI by enabling fast and cost-effective response generation in dialogue systems. However, dialogue systems based on neural generative approaches often lack truthfulness, reliability, and the ability to analyze the dialogue flow needed for smooth and consistent conversations with users. To address these issues, we introduce GroundHog, a modified BART architecture, to capture long multi-grained inputs gathered from various factual and linguistic sources, such as Abstract Meaning Representation, discourse relations, sentiment, and grounding information. For experiments, we present an automatically collected dataset from Reddit that includes multi-party conversations devoted to movies and TV series. The evaluation encompasses both automatic evaluation metrics and human evaluation. The obtained results demonstrate that using several linguistic inputs has the potential to enhance dialogue consistency, meaningfulness, and overall generation quality, even for automatically annotated data. We also provide an analysis that highlights the importance of individual linguistic features in interpreting the observed enhancements.</abstract>
    <identifier type="citekey">chernyavskiy-etal-2024-groundhog</identifier>
    <location>
        <url>https://aclanthology.org/2024.codi-1.14</url>
    </location>
    <part>
        <date>2024-03</date>
        <extent unit="page">
            <start>149</start>
            <end>160</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T GroundHog: Dialogue Generation using Multi-Grained Linguistic Input
%A Chernyavskiy, Alexander
%A Ostyakova, Lidiia
%A Ilvovsky, Dmitry
%Y Strube, Michael
%Y Braud, Chloe
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Loaiciga, Sharid
%Y Zeldes, Amir
%Y Li, Chuyuan
%S Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F chernyavskiy-etal-2024-groundhog
%X Recent language models have significantly boosted conversational AI by enabling fast and cost-effective response generation in dialogue systems. However, dialogue systems based on neural generative approaches often lack truthfulness, reliability, and the ability to analyze the dialogue flow needed for smooth and consistent conversations with users. To address these issues, we introduce GroundHog, a modified BART architecture, to capture long multi-grained inputs gathered from various factual and linguistic sources, such as Abstract Meaning Representation, discourse relations, sentiment, and grounding information. For experiments, we present an automatically collected dataset from Reddit that includes multi-party conversations devoted to movies and TV series. The evaluation encompasses both automatic evaluation metrics and human evaluation. The obtained results demonstrate that using several linguistic inputs has the potential to enhance dialogue consistency, meaningfulness, and overall generation quality, even for automatically annotated data. We also provide an analysis that highlights the importance of individual linguistic features in interpreting the observed enhancements.
%U https://aclanthology.org/2024.codi-1.14
%P 149-160

Markdown (Informal)

[GroundHog: Dialogue Generation using Multi-Grained Linguistic Input](https://aclanthology.org/2024.codi-1.14) (Chernyavskiy et al., CODI-WS 2024)

• GroundHog: Dialogue Generation using Multi-Grained Linguistic Input (Chernyavskiy et al., CODI-WS 2024)

ACL

• Alexander Chernyavskiy, Lidiia Ostyakova, and Dmitry Ilvovsky. 2024. GroundHog: Dialogue Generation using Multi-Grained Linguistic Input. In Proceedings of the 5th Workshop on Computational Approaches to Discourse (CODI 2024), pages 149–160, St. Julians, Malta. Association for Computational Linguistics.