<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="egea-gomez-etal-2021-syntax">
    <titleInfo>
        <title>Syntax-aware Transformers for Neural Machine Translation: The Case of Text to Sign Gloss Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Santiago</namePart>
        <namePart type="family">Egea Gómez</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Euan</namePart>
        <namePart type="family">McGill</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Horacio</namePart>
        <namePart type="family">Saggion</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Reinhard</namePart>
            <namePart type="family">Rapp</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Serge</namePart>
            <namePart type="family">Sharoff</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pierre</namePart>
            <namePart type="family">Zweigenbaum</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Online (Virtual Mode)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>It is well-established that the preferred mode of communication of the deaf and hard of hearing (DHH) community are Sign Languages (SLs), but they are considered low resource languages where natural language processing technologies are of concern. In this paper we study the problem of text to SL gloss Machine Translation (MT) using Transformer-based architectures. Despite the significant advances of MT for spoken languages in the recent couple of decades, MT is in its infancy when it comes to SLs. We enrich a Transformer-based architecture aggregating syntactic information extracted from a dependency parser to word-embeddings. We test our model on a well-known dataset showing that the syntax-aware model obtains performance gains in terms of MT evaluation metrics.</abstract>
    <identifier type="citekey">egea-gomez-etal-2021-syntax</identifier>
    <location>
        <url>https://aclanthology.org/2021.bucc-1.4</url>
    </location>
    <part>
        <date>2021-09</date>
        <extent unit="page">
            <start>18</start>
            <end>27</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Syntax-aware Transformers for Neural Machine Translation: The Case of Text to Sign Gloss Translation
%A Egea Gómez, Santiago
%A McGill, Euan
%A Saggion, Horacio
%Y Rapp, Reinhard
%Y Sharoff, Serge
%Y Zweigenbaum, Pierre
%S Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Online (Virtual Mode)
%F egea-gomez-etal-2021-syntax
%X It is well-established that the preferred mode of communication of the deaf and hard of hearing (DHH) community are Sign Languages (SLs), but they are considered low resource languages where natural language processing technologies are of concern. In this paper we study the problem of text to SL gloss Machine Translation (MT) using Transformer-based architectures. Despite the significant advances of MT for spoken languages in the recent couple of decades, MT is in its infancy when it comes to SLs. We enrich a Transformer-based architecture aggregating syntactic information extracted from a dependency parser to word-embeddings. We test our model on a well-known dataset showing that the syntax-aware model obtains performance gains in terms of MT evaluation metrics.
%U https://aclanthology.org/2021.bucc-1.4
%P 18-27

Markdown (Informal)

[Syntax-aware Transformers for Neural Machine Translation: The Case of Text to Sign Gloss Translation](https://aclanthology.org/2021.bucc-1.4) (Egea Gómez et al., BUCC 2021)

• Syntax-aware Transformers for Neural Machine Translation: The Case of Text to Sign Gloss Translation (Egea Gómez et al., BUCC 2021)

ACL

• Santiago Egea Gómez, Euan McGill, and Horacio Saggion. 2021. Syntax-aware Transformers for Neural Machine Translation: The Case of Text to Sign Gloss Translation. In Proceedings of the 14th Workshop on Building and Using Comparable Corpora (BUCC 2021), pages 18–27, Online (Virtual Mode). INCOMA Ltd..