<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ha-etal-2014-lexical">
    <titleInfo>
        <title>Lexical translation model using a deep neural network architecture</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Thanh-Le</namePart>
        <namePart type="family">Ha</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jan</namePart>
        <namePart type="family">Niehues</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alex</namePart>
        <namePart type="family">Waibel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2014-dec 4-5</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 11th International Workshop on Spoken Language Translation: Papers</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marcello</namePart>
            <namePart type="family">Federico</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sebastian</namePart>
            <namePart type="family">Stüker</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">François</namePart>
            <namePart type="family">Yvon</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <place>
                <placeTerm type="text">Lake Tahoe, California</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper we combine the advantages of a model using global source sentence contexts, the Discriminative Word Lexicon, and neural networks. By using deep neural networks instead of the linear maximum entropy model in the Discriminative Word Lexicon models, we are able to leverage dependencies between different source words due to the non-linearity. Furthermore, the models for different target words can share parameters and therefore data sparsity problems are effectively reduced. By using this approach in a state-of-the-art translation system, we can improve the performance by up to 0.5 BLEU points for three different language pairs on the TED translation task.</abstract>
    <identifier type="citekey">ha-etal-2014-lexical</identifier>
    <location>
        <url>https://aclanthology.org/2014.iwslt-papers.10</url>
    </location>
    <part>
        <date>2014-dec 4-5</date>
        <extent unit="page">
            <start>223</start>
            <end>229</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Lexical translation model using a deep neural network architecture
%A Ha, Thanh-Le
%A Niehues, Jan
%A Waibel, Alex
%Y Federico, Marcello
%Y Stüker, Sebastian
%Y Yvon, François
%S Proceedings of the 11th International Workshop on Spoken Language Translation: Papers
%D 2014
%8 dec 4 5
%C Lake Tahoe, California
%F ha-etal-2014-lexical
%X In this paper we combine the advantages of a model using global source sentence contexts, the Discriminative Word Lexicon, and neural networks. By using deep neural networks instead of the linear maximum entropy model in the Discriminative Word Lexicon models, we are able to leverage dependencies between different source words due to the non-linearity. Furthermore, the models for different target words can share parameters and therefore data sparsity problems are effectively reduced. By using this approach in a state-of-the-art translation system, we can improve the performance by up to 0.5 BLEU points for three different language pairs on the TED translation task.
%U https://aclanthology.org/2014.iwslt-papers.10
%P 223-229

Markdown (Informal)

[Lexical translation model using a deep neural network architecture](https://aclanthology.org/2014.iwslt-papers.10) (Ha et al., IWSLT 2014)

• Lexical translation model using a deep neural network architecture (Ha et al., IWSLT 2014)

ACL

• Thanh-Le Ha, Jan Niehues, and Alex Waibel. 2014. Lexical translation model using a deep neural network architecture. In Proceedings of the 11th International Workshop on Spoken Language Translation: Papers, pages 223–229, Lake Tahoe, California.