<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="riesa-yarowsky-2006-minimally">
    <titleInfo>
        <title>Minimally Supervised Morphological Segmentation with Applications to Machine Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jason</namePart>
        <namePart type="family">Riesa</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">David</namePart>
        <namePart type="family">Yarowsky</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>Inflected languages in a low-resource setting present a data sparsity problem for statistical machine translation. In this paper, we present a minimally supervised algorithm for morpheme segmentation on Arabic dialects which reduces unknown words at translation time by over 50%, total vocabulary size by over 40%, and yields a significant increase in BLEU score over a previous state-of-the-art phrase-based statistical MT system.</abstract>
    <identifier type="citekey">riesa-yarowsky-2006-minimally</identifier>
    <location>
        <url>https://aclanthology.org/2006.amta-papers.21</url>
    </location>
    <part>
        <date>2006-aug 8-12</date>
        <extent unit="page">
            <start>185</start>
            <end>192</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Minimally Supervised Morphological Segmentation with Applications to Machine Translation
%A Riesa, Jason
%A Yarowsky, David
%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 riesa-yarowsky-2006-minimally
%X Inflected languages in a low-resource setting present a data sparsity problem for statistical machine translation. In this paper, we present a minimally supervised algorithm for morpheme segmentation on Arabic dialects which reduces unknown words at translation time by over 50%, total vocabulary size by over 40%, and yields a significant increase in BLEU score over a previous state-of-the-art phrase-based statistical MT system.
%U https://aclanthology.org/2006.amta-papers.21
%P 185-192

Markdown (Informal)

[Minimally Supervised Morphological Segmentation with Applications to Machine Translation](https://aclanthology.org/2006.amta-papers.21) (Riesa & Yarowsky, AMTA 2006)

• Minimally Supervised Morphological Segmentation with Applications to Machine Translation (Riesa & Yarowsky, AMTA 2006)

ACL

• Jason Riesa and David Yarowsky. 2006. Minimally Supervised Morphological Segmentation with Applications to Machine Translation. In Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers, pages 185–192, Cambridge, Massachusetts, USA. Association for Machine Translation in the Americas.