<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="lo-etal-2013-improving-machine">
    <titleInfo>
        <title>Improving machine translation into Chinese by tuning against Chinese MEANT</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Chi-kiu</namePart>
        <namePart type="family">Lo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Meriem</namePart>
        <namePart type="family">Beloucif</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Dekai</namePart>
        <namePart type="family">Wu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2013-dec 5-6</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Joy</namePart>
            <namePart type="given">Ying</namePart>
            <namePart type="family">Zhang</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <place>
                <placeTerm type="text">Heidelberg, Germany</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We present the first ever results showing that Chinese MT output is significantly improved by tuning a MT system against a semantic frame based objective function, MEANT, rather than an n-gram based objective function, BLEU, as measured across commonly used metrics and different test sets. Recent work showed that by preserving the meaning of the translations as captured by semantic frames in the training process, MT systems for translating into English on both formal and informal genres are constrained to produce more adequate translations by making more accurate choices on lexical output and reordering rules. In this paper we describe our experiments in IWSLT 2013 TED talk MT tasks on tuning MT systems against MEANT for translating into Chinese and English respectively. We show that the Chinese translation output benefits more from tuning a MT system against MEANT than the English translation output due to the ambiguous nature of word boundaries in Chinese. Our encouraging results show that using MEANT is a promising alternative to BLEU in both evaluating and tuning MT systems to drive the progress of MT research across different languages.</abstract>
    <identifier type="citekey">lo-etal-2013-improving-machine</identifier>
    <location>
        <url>https://aclanthology.org/2013.iwslt-evaluation.5</url>
    </location>
    <part>
        <date>2013-dec 5-6</date>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Improving machine translation into Chinese by tuning against Chinese MEANT
%A Lo, Chi-kiu
%A Beloucif, Meriem
%A Wu, Dekai
%Y Zhang, Joy Ying
%S Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign
%D 2013
%8 dec 5 6
%C Heidelberg, Germany
%F lo-etal-2013-improving-machine
%X We present the first ever results showing that Chinese MT output is significantly improved by tuning a MT system against a semantic frame based objective function, MEANT, rather than an n-gram based objective function, BLEU, as measured across commonly used metrics and different test sets. Recent work showed that by preserving the meaning of the translations as captured by semantic frames in the training process, MT systems for translating into English on both formal and informal genres are constrained to produce more adequate translations by making more accurate choices on lexical output and reordering rules. In this paper we describe our experiments in IWSLT 2013 TED talk MT tasks on tuning MT systems against MEANT for translating into Chinese and English respectively. We show that the Chinese translation output benefits more from tuning a MT system against MEANT than the English translation output due to the ambiguous nature of word boundaries in Chinese. Our encouraging results show that using MEANT is a promising alternative to BLEU in both evaluating and tuning MT systems to drive the progress of MT research across different languages.
%U https://aclanthology.org/2013.iwslt-evaluation.5

Markdown (Informal)

[Improving machine translation into Chinese by tuning against Chinese MEANT](https://aclanthology.org/2013.iwslt-evaluation.5) (Lo et al., IWSLT 2013)

• Improving machine translation into Chinese by tuning against Chinese MEANT (Lo et al., IWSLT 2013)

ACL

• Chi-kiu Lo, Meriem Beloucif, and Dekai Wu. 2013. Improving machine translation into Chinese by tuning against Chinese MEANT. In Proceedings of the 10th International Workshop on Spoken Language Translation: Evaluation Campaign, Heidelberg, Germany.