<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lin-etal-2022-trend">
    <titleInfo>
        <title>TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Po-Wei</namePart>
        <namePart type="family">Lin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Shang-Yu</namePart>
        <namePart type="family">Su</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yun-Nung</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Oliver</namePart>
            <namePart type="family">Lemon</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Dilek</namePart>
            <namePart type="family">Hakkani-Tur</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">Arash</namePart>
            <namePart type="family">Ashrafzadeh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="given">Hernández</namePart>
            <namePart type="family">Garcia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Malihe</namePart>
            <namePart type="family">Alikhani</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Vandyke</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ondřej</namePart>
            <namePart type="family">Dušek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Edinburgh, UK</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The goal of dialogue relation extraction (DRE) is to identify the relation between two entities in a given dialogue. During conversations, speakers may expose their relations to certain entities by explicit or implicit clues, such evidences called “triggers”. However, trigger annotations may not be always available for the target data, so it is challenging to leverage such information for enhancing the performance. Therefore, this paper proposes to learn how to identify triggers from the data with trigger annotations and then transfers the trigger-finding capability to other datasets for better performance. The experiments show that the proposed approach is capable of improving relation extraction performance of unseen relations and also demonstrate the transferability of our proposed trigger-finding model across different domains and datasets.</abstract>
    <identifier type="citekey">lin-etal-2022-trend</identifier>
    <identifier type="doi">10.18653/v1/2022.sigdial-1.58</identifier>
    <location>
        <url>https://aclanthology.org/2022.sigdial-1.58</url>
    </location>
    <part>
        <date>2022-09</date>
        <extent unit="page">
            <start>623</start>
            <end>629</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues
%A Lin, Po-Wei
%A Su, Shang-Yu
%A Chen, Yun-Nung
%Y Lemon, Oliver
%Y Hakkani-Tur, Dilek
%Y Li, Junyi Jessy
%Y Ashrafzadeh, Arash
%Y Garcia, Daniel Hernández
%Y Alikhani, Malihe
%Y Vandyke, David
%Y Dušek, Ondřej
%S Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2022
%8 September
%I Association for Computational Linguistics
%C Edinburgh, UK
%F lin-etal-2022-trend
%X The goal of dialogue relation extraction (DRE) is to identify the relation between two entities in a given dialogue. During conversations, speakers may expose their relations to certain entities by explicit or implicit clues, such evidences called “triggers”. However, trigger annotations may not be always available for the target data, so it is challenging to leverage such information for enhancing the performance. Therefore, this paper proposes to learn how to identify triggers from the data with trigger annotations and then transfers the trigger-finding capability to other datasets for better performance. The experiments show that the proposed approach is capable of improving relation extraction performance of unseen relations and also demonstrate the transferability of our proposed trigger-finding model across different domains and datasets.
%R 10.18653/v1/2022.sigdial-1.58
%U https://aclanthology.org/2022.sigdial-1.58
%U https://doi.org/10.18653/v1/2022.sigdial-1.58
%P 623-629

Markdown (Informal)

[TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues](https://aclanthology.org/2022.sigdial-1.58) (Lin et al., SIGDIAL 2022)

• TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues (Lin et al., SIGDIAL 2022)

ACL

• Po-Wei Lin, Shang-Yu Su, and Yun-Nung Chen. 2022. TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 623–629, Edinburgh, UK. Association for Computational Linguistics.