<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yerukola-etal-2021-data">
    <titleInfo>
        <title>Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Akhila</namePart>
        <namePart type="family">Yerukola</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mason</namePart>
        <namePart type="family">Bretan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hongxia</namePart>
        <namePart type="family">Jin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-04</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Paola</namePart>
            <namePart type="family">Merlo</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jorg</namePart>
            <namePart type="family">Tiedemann</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Reut</namePart>
            <namePart type="family">Tsarfaty</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We introduce a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. We compare and evaluate this method with a range of augmentation techniques encompassing generative models such as VAEs and performance-boosting techniques such as synonym replacement and back-translation. We show our method performs strongly on domain and intent classification tasks for a voice assistant and in a user-study focused on utterance naturalness and semantic similarity.</abstract>
    <identifier type="citekey">yerukola-etal-2021-data</identifier>
    <identifier type="doi">10.18653/v1/2021.eacl-main.159</identifier>
    <location>
        <url>https://aclanthology.org/2021.eacl-main.159</url>
    </location>
    <part>
        <date>2021-04</date>
        <extent unit="page">
            <start>1852</start>
            <end>1860</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase
%A Yerukola, Akhila
%A Bretan, Mason
%A Jin, Hongxia
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F yerukola-etal-2021-data
%X We introduce a data augmentation technique based on byte pair encoding and a BERT-like self-attention model to boost performance on spoken language understanding tasks. We compare and evaluate this method with a range of augmentation techniques encompassing generative models such as VAEs and performance-boosting techniques such as synonym replacement and back-translation. We show our method performs strongly on domain and intent classification tasks for a voice assistant and in a user-study focused on utterance naturalness and semantic similarity.
%R 10.18653/v1/2021.eacl-main.159
%U https://aclanthology.org/2021.eacl-main.159
%U https://doi.org/10.18653/v1/2021.eacl-main.159
%P 1852-1860

Markdown (Informal)

[Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase](https://aclanthology.org/2021.eacl-main.159) (Yerukola et al., EACL 2021)

• Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase (Yerukola et al., EACL 2021)

ACL

• Akhila Yerukola, Mason Bretan, and Hongxia Jin. 2021. Data Augmentation for Voice-Assistant NLU using BERT-based Interchangeable Rephrase. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1852–1860, Online. Association for Computational Linguistics.