<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dalvi-etal-2018-incremental">
    <titleInfo>
        <title>Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Fahim</namePart>
        <namePart type="family">Dalvi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nadir</namePart>
        <namePart type="family">Durrani</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hassan</namePart>
        <namePart type="family">Sajjad</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Stephan</namePart>
        <namePart type="family">Vogel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Marilyn</namePart>
            <namePart type="family">Walker</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Heng</namePart>
            <namePart type="family">Ji</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Amanda</namePart>
            <namePart type="family">Stent</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">New Orleans, Louisiana</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We address the problem of simultaneous translation by modifying the Neural MT decoder to operate with dynamically built encoder and attention. We propose a tunable agent which decides the best segmentation strategy for a user-defined BLEU loss and Average Proportion (AP) constraint. Our agent outperforms previously proposed Wait-if-diff and Wait-if-worse agents (Cho and Esipova, 2016) on BLEU with a lower latency. Secondly we proposed data-driven changes to Neural MT training to better match the incremental decoding framework.</abstract>
    <identifier type="citekey">dalvi-etal-2018-incremental</identifier>
    <identifier type="doi">10.18653/v1/N18-2079</identifier>
    <location>
        <url>https://aclanthology.org/N18-2079</url>
    </location>
    <part>
        <date>2018-06</date>
        <extent unit="page">
            <start>493</start>
            <end>499</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation
%A Dalvi, Fahim
%A Durrani, Nadir
%A Sajjad, Hassan
%A Vogel, Stephan
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F dalvi-etal-2018-incremental
%X We address the problem of simultaneous translation by modifying the Neural MT decoder to operate with dynamically built encoder and attention. We propose a tunable agent which decides the best segmentation strategy for a user-defined BLEU loss and Average Proportion (AP) constraint. Our agent outperforms previously proposed Wait-if-diff and Wait-if-worse agents (Cho and Esipova, 2016) on BLEU with a lower latency. Secondly we proposed data-driven changes to Neural MT training to better match the incremental decoding framework.
%R 10.18653/v1/N18-2079
%U https://aclanthology.org/N18-2079
%U https://doi.org/10.18653/v1/N18-2079
%P 493-499

Markdown (Informal)

[Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation](https://aclanthology.org/N18-2079) (Dalvi et al., NAACL 2018)

• Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation (Dalvi et al., NAACL 2018)

ACL

• Fahim Dalvi, Nadir Durrani, Hassan Sajjad, and Stephan Vogel. 2018. Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 493–499, New Orleans, Louisiana. Association for Computational Linguistics.