<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="matusov-kopru-2010-improving">
    <titleInfo>
        <title>Improving Reordering in Statistical Machine Translation from Farsi</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Evgeny</namePart>
        <namePart type="family">Matusov</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Selçuk</namePart>
        <namePart type="family">Köprü</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2010-oct 31-nov 4</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 9th 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">Denver, Colorado, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we propose a novel model for scoring reordering in phrase-based statistical machine translation (SMT) and successfully use it for translation from Farsi into English and Arabic. The model replaces the distance-based distortion model that is widely used in most SMT systems. The main idea of the model is to penalize each new deviation from the monotonic translation path. We also propose a way for combining this model with manually created reordering rules for Farsi which try to alleviate the difference in sentence structure between Farsi and English/Arabic by changing the position of the verb. The rules are used in the SMT search as soft constraints. In the experiments on two general-domain translation tasks, the proposed penalty-based model improves the BLEU score by up to 1.5% absolute as compared to the baseline of monotonic translation, and up to 1.2% as compared to using the distance-based distortion model.</abstract>
    <identifier type="citekey">matusov-kopru-2010-improving</identifier>
    <location>
        <url>https://aclanthology.org/2010.amta-papers.29</url>
    </location>
    <part>
        <date>2010-oct 31-nov 4</date>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Improving Reordering in Statistical Machine Translation from Farsi
%A Matusov, Evgeny
%A Köprü, Selçuk
%S Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2010
%8 oct 31 nov 4
%I Association for Machine Translation in the Americas
%C Denver, Colorado, USA
%F matusov-kopru-2010-improving
%X In this paper, we propose a novel model for scoring reordering in phrase-based statistical machine translation (SMT) and successfully use it for translation from Farsi into English and Arabic. The model replaces the distance-based distortion model that is widely used in most SMT systems. The main idea of the model is to penalize each new deviation from the monotonic translation path. We also propose a way for combining this model with manually created reordering rules for Farsi which try to alleviate the difference in sentence structure between Farsi and English/Arabic by changing the position of the verb. The rules are used in the SMT search as soft constraints. In the experiments on two general-domain translation tasks, the proposed penalty-based model improves the BLEU score by up to 1.5% absolute as compared to the baseline of monotonic translation, and up to 1.2% as compared to using the distance-based distortion model.
%U https://aclanthology.org/2010.amta-papers.29

Markdown (Informal)

[Improving Reordering in Statistical Machine Translation from Farsi](https://aclanthology.org/2010.amta-papers.29) (Matusov & Köprü, AMTA 2010)

• Improving Reordering in Statistical Machine Translation from Farsi (Matusov & Köprü, AMTA 2010)

ACL

• Evgeny Matusov and Selçuk Köprü. 2010. Improving Reordering in Statistical Machine Translation from Farsi. In Proceedings of the 9th Conference of the Association for Machine Translation in the Americas: Research Papers, Denver, Colorado, USA. Association for Machine Translation in the Americas.