<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="birch-etal-2006-constraining-phrase">
    <titleInfo>
        <title>Constraining the Phrase-Based, Joint Probability Statistical Translation Model</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Alexandra</namePart>
        <namePart type="family">Birch</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Chris</namePart>
        <namePart type="family">Callison-Burch</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Miles</namePart>
        <namePart type="family">Osborne</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2006-aug 8-12</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Machine Translation in the Americas</publisher>
            <place>
                <placeTerm type="text">Cambridge, Massachusetts, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The Joint Probability Model proposed by Marcu and Wong (2002) provides a probabilistic framework for modeling phrase-based statistical machine transla- tion (SMT). The model’s usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present a method of constraining the search space of the Joint Probability Model based on statistically and linguistically motivated word align- ments. This method reduces the complexity and size of the Joint Model and allows it to display performance superior to the standard phrase-based models for small amounts of training material.</abstract>
    <identifier type="citekey">birch-etal-2006-constraining-phrase</identifier>
    <location>
        <url>https://aclanthology.org/2006.amta-papers.2</url>
    </location>
    <part>
        <date>2006-aug 8-12</date>
        <extent unit="page">
            <start>10</start>
            <end>18</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Constraining the Phrase-Based, Joint Probability Statistical Translation Model
%A Birch, Alexandra
%A Callison-Burch, Chris
%A Osborne, Miles
%S Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers
%D 2006
%8 aug 8 12
%I Association for Machine Translation in the Americas
%C Cambridge, Massachusetts, USA
%F birch-etal-2006-constraining-phrase
%X The Joint Probability Model proposed by Marcu and Wong (2002) provides a probabilistic framework for modeling phrase-based statistical machine transla- tion (SMT). The model’s usefulness is, however, limited by the computational complexity of estimating parameters at the phrase level. We present a method of constraining the search space of the Joint Probability Model based on statistically and linguistically motivated word align- ments. This method reduces the complexity and size of the Joint Model and allows it to display performance superior to the standard phrase-based models for small amounts of training material.
%U https://aclanthology.org/2006.amta-papers.2
%P 10-18

Markdown (Informal)

[Constraining the Phrase-Based, Joint Probability Statistical Translation Model](https://aclanthology.org/2006.amta-papers.2) (Birch et al., AMTA 2006)

• Constraining the Phrase-Based, Joint Probability Statistical Translation Model (Birch et al., AMTA 2006)

ACL

• Alexandra Birch, Chris Callison-Burch, and Miles Osborne. 2006. Constraining the Phrase-Based, Joint Probability Statistical Translation Model. In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 10–18, Cambridge, Massachusetts, USA. Association for Machine Translation in the Americas.