<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nakazawa-kurohashi-2008-linguistically">
    <titleInfo>
        <title>Linguistically-motivated Tree-based Probabilistic Phrase Alignment</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Toshiaki</namePart>
        <namePart type="family">Nakazawa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sadao</namePart>
        <namePart type="family">Kurohashi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2008-oct 21-25</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Machine Translation in the Americas</publisher>
            <place>
                <placeTerm type="text">Waikiki, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we propose a probabilistic phrase alignment model based on dependency trees. This model is linguistically-motivated, using syntactic information during alignment process. The main advantage of this model is that the linguistic difference between source and target languages is successfully absorbed. It is composed of two models: Model1 is using content word translation probability and function word translation probability; Model2 uses dependency relation probability which is defined for a pair of positional relations on dependency trees. Relation probability acts as tree-based phrase reordering model. Since this model is directed, we combine two alignment results from bi-directional training by symmetrization heuristics to get definitive alignment. We conduct experiments on a Japanese-English corpus, and achieve reasonably high quality of alignment compared with word-based alignment model.</abstract>
    <identifier type="citekey">nakazawa-kurohashi-2008-linguistically</identifier>
    <location>
        <url>https://aclanthology.org/2008.amta-papers.15</url>
    </location>
    <part>
        <date>2008-oct 21-25</date>
        <extent unit="page">
            <start>163</start>
            <end>171</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Linguistically-motivated Tree-based Probabilistic Phrase Alignment
%A Nakazawa, Toshiaki
%A Kurohashi, Sadao
%S Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2008
%8 oct 21 25
%I Association for Machine Translation in the Americas
%C Waikiki, USA
%F nakazawa-kurohashi-2008-linguistically
%X In this paper, we propose a probabilistic phrase alignment model based on dependency trees. This model is linguistically-motivated, using syntactic information during alignment process. The main advantage of this model is that the linguistic difference between source and target languages is successfully absorbed. It is composed of two models: Model1 is using content word translation probability and function word translation probability; Model2 uses dependency relation probability which is defined for a pair of positional relations on dependency trees. Relation probability acts as tree-based phrase reordering model. Since this model is directed, we combine two alignment results from bi-directional training by symmetrization heuristics to get definitive alignment. We conduct experiments on a Japanese-English corpus, and achieve reasonably high quality of alignment compared with word-based alignment model.
%U https://aclanthology.org/2008.amta-papers.15
%P 163-171

Markdown (Informal)

[Linguistically-motivated Tree-based Probabilistic Phrase Alignment](https://aclanthology.org/2008.amta-papers.15) (Nakazawa & Kurohashi, AMTA 2008)

• Linguistically-motivated Tree-based Probabilistic Phrase Alignment (Nakazawa & Kurohashi, AMTA 2008)

ACL

• Toshiaki Nakazawa and Sadao Kurohashi. 2008. Linguistically-motivated Tree-based Probabilistic Phrase Alignment. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers, pages 163–171, Waikiki, USA. Association for Machine Translation in the Americas.