<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jang-etal-2021-bpm">
    <titleInfo>
        <title>BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jin</namePart>
        <namePart type="given">Yea</namePart>
        <namePart type="family">Jang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">San</namePart>
        <namePart type="family">Kim</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Minyoung</namePart>
        <namePart type="family">Jung</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Saim</namePart>
        <namePart type="family">Shin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gahgene</namePart>
        <namePart type="family">Gweon</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Scott</namePart>
            <namePart type="given">Wen-tau</namePart>
            <namePart type="family">Yih</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online and Punta Cana, Dominican Republic</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Backchannel (BC), a short reaction signal of a listener to a speaker’s utterances, helps to improve the quality of the conversation. Several studies have been conducted to predict BC in conversation; however, the utilization of advanced natural language processing techniques using lexical information presented in the utterances of a speaker has been less considered. To address this limitation, we present a BC prediction model called BPM_MT (Backchannel prediction model with multitask learning), which utilizes KoBERT, a pre-trained language model. The BPM_MT simultaneously carries out two tasks at learning: 1) BC category prediction using acoustic and lexical features, and 2) sentiment score prediction based on sentiment cues. BPM_MT exhibited 14.24% performance improvement compared to the existing baseline in the four BC categories: continuer, understanding, empathic response, and No BC. In particular, for empathic response category, a performance improvement of 17.14% was achieved.</abstract>
    <identifier type="citekey">jang-etal-2021-bpm</identifier>
    <identifier type="doi">10.18653/v1/2021.emnlp-main.277</identifier>
    <location>
        <url>https://aclanthology.org/2021.emnlp-main.277</url>
    </location>
    <part>
        <date>2021-11</date>
        <extent unit="page">
            <start>3447</start>
            <end>3452</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning
%A Jang, Jin Yea
%A Kim, San
%A Jung, Minyoung
%A Shin, Saim
%A Gweon, Gahgene
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F jang-etal-2021-bpm
%X Backchannel (BC), a short reaction signal of a listener to a speaker’s utterances, helps to improve the quality of the conversation. Several studies have been conducted to predict BC in conversation; however, the utilization of advanced natural language processing techniques using lexical information presented in the utterances of a speaker has been less considered. To address this limitation, we present a BC prediction model called BPM_MT (Backchannel prediction model with multitask learning), which utilizes KoBERT, a pre-trained language model. The BPM_MT simultaneously carries out two tasks at learning: 1) BC category prediction using acoustic and lexical features, and 2) sentiment score prediction based on sentiment cues. BPM_MT exhibited 14.24% performance improvement compared to the existing baseline in the four BC categories: continuer, understanding, empathic response, and No BC. In particular, for empathic response category, a performance improvement of 17.14% was achieved.
%R 10.18653/v1/2021.emnlp-main.277
%U https://aclanthology.org/2021.emnlp-main.277
%U https://doi.org/10.18653/v1/2021.emnlp-main.277
%P 3447-3452

Markdown (Informal)

[BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning](https://aclanthology.org/2021.emnlp-main.277) (Jang et al., EMNLP 2021)

• BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning (Jang et al., EMNLP 2021)

ACL

• Jin Yea Jang, San Kim, Minyoung Jung, Saim Shin, and Gahgene Gweon. 2021. BPM_MT: Enhanced Backchannel Prediction Model using Multi-Task Learning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3447–3452, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.