<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="wu-etal-2005-improving">
    <titleInfo>
        <title>Improving Translation Memory with Word Alignment Information</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hua</namePart>
        <namePart type="family">Wu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Haifeng</namePart>
        <namePart type="family">Wang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhanyi</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kai</namePart>
        <namePart type="family">Tang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2005-sep 13-15</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of Machine Translation Summit X: Posters</title>
        </titleInfo>
        <originInfo>
            <place>
                <placeTerm type="text">Phuket, Thailand</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>This paper describes a generalized translation memory system, which takes advantage of sentence level matching, sub-sentential matching, and pattern-based machine translation technologies. All of the three techniques generate translation suggestions with the assistance of word alignment information. For the sentence level matching, the system generates the translation suggestion by modifying the translations of the most similar example with word alignment information. For sub-sentential matching, the system locates the translation fragments in several examples with word alignment information, and then generates the translation suggestion by combining these translation fragments. For pattern-based machine translation, the system first extracts translation patterns from examples using word alignment information and then generates translation suggestions with pattern matching. This system is compared with a traditional translation memory system without word alignment information in terms of translation efficiency and quality. Evaluation results indicate that our system improves the translation quality and saves about 20% translation time.</abstract>
    <identifier type="citekey">wu-etal-2005-improving</identifier>
    <location>
        <url>https://aclanthology.org/2005.mtsummit-posters.7/</url>
    </location>
    <part>
        <date>2005-sep 13-15</date>
        <extent unit="page">
            <start>364</start>
            <end>371</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Improving Translation Memory with Word Alignment Information
%A Wu, Hua
%A Wang, Haifeng
%A Liu, Zhanyi
%A Tang, Kai
%S Proceedings of Machine Translation Summit X: Posters
%D 2005
%8 sep 13 15
%C Phuket, Thailand
%F wu-etal-2005-improving
%X This paper describes a generalized translation memory system, which takes advantage of sentence level matching, sub-sentential matching, and pattern-based machine translation technologies. All of the three techniques generate translation suggestions with the assistance of word alignment information. For the sentence level matching, the system generates the translation suggestion by modifying the translations of the most similar example with word alignment information. For sub-sentential matching, the system locates the translation fragments in several examples with word alignment information, and then generates the translation suggestion by combining these translation fragments. For pattern-based machine translation, the system first extracts translation patterns from examples using word alignment information and then generates translation suggestions with pattern matching. This system is compared with a traditional translation memory system without word alignment information in terms of translation efficiency and quality. Evaluation results indicate that our system improves the translation quality and saves about 20% translation time.
%U https://aclanthology.org/2005.mtsummit-posters.7/
%P 364-371

Markdown (Informal)

[Improving Translation Memory with Word Alignment Information](https://aclanthology.org/2005.mtsummit-posters.7/) (Wu et al., MTSummit 2005)

• Improving Translation Memory with Word Alignment Information (Wu et al., MTSummit 2005)

ACL

• Hua Wu, Haifeng Wang, Zhanyi Liu, and Kai Tang. 2005. Improving Translation Memory with Word Alignment Information. In Proceedings of Machine Translation Summit X: Posters, pages 364–371, Phuket, Thailand.