<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="flanagan-2014-bilingual">
    <titleInfo>
        <title>Bilingual phrase-to-phrase alignment for arbitrarily-small datasets</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kevin</namePart>
        <namePart type="family">Flanagan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2014-oct 22-26</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Michel</namePart>
            <namePart type="family">Simard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Machine Translation in the Americas</publisher>
            <place>
                <placeTerm type="text">Vancouver, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper presents a novel system for sub-sentential alignment of bilingual sentence pairs, however few, using readily-available machine-readable bilingual dictionaries. Performance is evaluated against an existing gold-standard parallel corpus where word alignments are annotated, showing results that are a considerable improvement on a comparable system and on GIZA++ performance for the same corpus. Since naïve application of the system for N languages would require N(N - 1) dictionaries, it is also evaluated using a pivot language, where only 2(N - 1) dictionaries would be required, with surprisingly similar performance. The system is proposed as an alternative to statistical methods, for use with very small corpora or for ‘on-the-fly’ alignment.</abstract>
    <identifier type="citekey">flanagan-2014-bilingual</identifier>
    <location>
        <url>https://aclanthology.org/2014.amta-researchers.7</url>
    </location>
    <part>
        <date>2014-oct 22-26</date>
        <extent unit="page">
            <start>83</start>
            <end>95</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Bilingual phrase-to-phrase alignment for arbitrarily-small datasets
%A Flanagan, Kevin
%Y Al-Onaizan, Yaser
%Y Simard, Michel
%S Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track
%D 2014
%8 oct 22 26
%I Association for Machine Translation in the Americas
%C Vancouver, Canada
%F flanagan-2014-bilingual
%X This paper presents a novel system for sub-sentential alignment of bilingual sentence pairs, however few, using readily-available machine-readable bilingual dictionaries. Performance is evaluated against an existing gold-standard parallel corpus where word alignments are annotated, showing results that are a considerable improvement on a comparable system and on GIZA++ performance for the same corpus. Since naïve application of the system for N languages would require N(N - 1) dictionaries, it is also evaluated using a pivot language, where only 2(N - 1) dictionaries would be required, with surprisingly similar performance. The system is proposed as an alternative to statistical methods, for use with very small corpora or for ‘on-the-fly’ alignment.
%U https://aclanthology.org/2014.amta-researchers.7
%P 83-95

Markdown (Informal)

[Bilingual phrase-to-phrase alignment for arbitrarily-small datasets](https://aclanthology.org/2014.amta-researchers.7) (Flanagan, AMTA 2014)

• Bilingual phrase-to-phrase alignment for arbitrarily-small datasets (Flanagan, AMTA 2014)

ACL

• Kevin Flanagan. 2014. Bilingual phrase-to-phrase alignment for arbitrarily-small datasets. In Proceedings of the 11th Conference of the Association for Machine Translation in the Americas: MT Researchers Track, pages 83–95, Vancouver, Canada. Association for Machine Translation in the Americas.