<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hammond-2021-data">
    <titleInfo>
        <title>Data augmentation for low-resource grapheme-to-phoneme mapping</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Michael</namePart>
        <namePart type="family">Hammond</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Garrett</namePart>
            <namePart type="family">Nicolai</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kyle</namePart>
            <namePart type="family">Gorman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Ryan</namePart>
            <namePart type="family">Cotterell</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>In this paper we explore a very simple neural approach to mapping orthography to phonetic transcription in a low-resource context. The basic idea is to start from a baseline system and focus all efforts on data augmentation. We will see that some techniques work, but others do not.</abstract>
    <identifier type="citekey">hammond-2021-data</identifier>
    <identifier type="doi">10.18653/v1/2021.sigmorphon-1.14</identifier>
    <location>
        <url>https://aclanthology.org/2021.sigmorphon-1.14</url>
    </location>
    <part>
        <date>2021-08</date>
        <extent unit="page">
            <start>126</start>
            <end>130</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Data augmentation for low-resource grapheme-to-phoneme mapping
%A Hammond, Michael
%Y Nicolai, Garrett
%Y Gorman, Kyle
%Y Cotterell, Ryan
%S Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F hammond-2021-data
%X In this paper we explore a very simple neural approach to mapping orthography to phonetic transcription in a low-resource context. The basic idea is to start from a baseline system and focus all efforts on data augmentation. We will see that some techniques work, but others do not.
%R 10.18653/v1/2021.sigmorphon-1.14
%U https://aclanthology.org/2021.sigmorphon-1.14
%U https://doi.org/10.18653/v1/2021.sigmorphon-1.14
%P 126-130

Markdown (Informal)

[Data augmentation for low-resource grapheme-to-phoneme mapping](https://aclanthology.org/2021.sigmorphon-1.14) (Hammond, SIGMORPHON 2021)

• Data augmentation for low-resource grapheme-to-phoneme mapping (Hammond, SIGMORPHON 2021)

ACL

• Michael Hammond. 2021. Data augmentation for low-resource grapheme-to-phoneme mapping. In Proceedings of the 18th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pages 126–130, Online. Association for Computational Linguistics.