<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="stein-etal-2010-sign">
    <titleInfo>
        <title>Sign language machine translation overkill</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Stein</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Christoph</namePart>
        <namePart type="family">Schmidt</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hermann</namePart>
        <namePart type="family">Ney</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2010-dec 2-3</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 7th International Workshop on Spoken Language Translation: Papers</title>
        </titleInfo>
        <originInfo>
            <place>
                <placeTerm type="text">Paris, France</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Sign languages represent an interesting niche for statistical machine translation that is typically hampered by the scarceness of suitable data, and most papers in this area apply only a few, well-known techniques and do not adapt them to small-sized corpora. In this paper, we will propose new methods for common approaches like scaling factor optimization and alignment merging strategies which helped improve our baseline. We also conduct experiments with different decoders and employ state-of-the-art techniques like soft syntactic labels as well as trigger-based and discriminative word lexica and system combination. All methods are evaluated on one of the largest sign language corpora available.</abstract>
    <identifier type="citekey">stein-etal-2010-sign</identifier>
    <location>
        <url>https://aclanthology.org/2010.iwslt-papers.17</url>
    </location>
    <part>
        <date>2010-dec 2-3</date>
        <extent unit="page">
            <start>337</start>
            <end>344</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Sign language machine translation overkill
%A Stein, Daniel
%A Schmidt, Christoph
%A Ney, Hermann
%S Proceedings of the 7th International Workshop on Spoken Language Translation: Papers
%D 2010
%8 dec 2 3
%C Paris, France
%F stein-etal-2010-sign
%X Sign languages represent an interesting niche for statistical machine translation that is typically hampered by the scarceness of suitable data, and most papers in this area apply only a few, well-known techniques and do not adapt them to small-sized corpora. In this paper, we will propose new methods for common approaches like scaling factor optimization and alignment merging strategies which helped improve our baseline. We also conduct experiments with different decoders and employ state-of-the-art techniques like soft syntactic labels as well as trigger-based and discriminative word lexica and system combination. All methods are evaluated on one of the largest sign language corpora available.
%U https://aclanthology.org/2010.iwslt-papers.17
%P 337-344

Markdown (Informal)

[Sign language machine translation overkill](https://aclanthology.org/2010.iwslt-papers.17) (Stein et al., IWSLT 2010)

• Sign language machine translation overkill (Stein et al., IWSLT 2010)

ACL

• Daniel Stein, Christoph Schmidt, and Hermann Ney. 2010. Sign language machine translation overkill. In Proceedings of the 7th International Workshop on Spoken Language Translation: Papers, pages 337–344, Paris, France.