<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yang-etal-2019-maam">
    <titleInfo>
        <title>MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Pengcheng</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Fuli</namePart>
        <namePart type="family">Luo</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Peng</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tianyu</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xu</namePart>
        <namePart type="family">Sun</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Traum</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lluís</namePart>
            <namePart type="family">Màrquez</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The task of unsupervised bilingual lexicon induction (UBLI) aims to induce word translations from monolingual corpora in two languages. Previous work has shown that morphological variation is an intractable challenge for the UBLI task, where the induced translation in failure case is usually morphologically related to the correct translation. To tackle this challenge, we propose a morphology-aware alignment model for the UBLI task. The proposed model aims to alleviate the adverse effect of morphological variation by introducing grammatical information learned by the pre-trained denoising language model. Results show that our approach can substantially outperform several state-of-the-art unsupervised systems, and even achieves competitive performance compared to supervised methods.</abstract>
    <identifier type="citekey">yang-etal-2019-maam</identifier>
    <identifier type="doi">10.18653/v1/P19-1308</identifier>
    <location>
        <url>https://aclanthology.org/P19-1308</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>3190</start>
            <end>3196</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction
%A Yang, Pengcheng
%A Luo, Fuli
%A Chen, Peng
%A Liu, Tianyu
%A Sun, Xu
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F yang-etal-2019-maam
%X The task of unsupervised bilingual lexicon induction (UBLI) aims to induce word translations from monolingual corpora in two languages. Previous work has shown that morphological variation is an intractable challenge for the UBLI task, where the induced translation in failure case is usually morphologically related to the correct translation. To tackle this challenge, we propose a morphology-aware alignment model for the UBLI task. The proposed model aims to alleviate the adverse effect of morphological variation by introducing grammatical information learned by the pre-trained denoising language model. Results show that our approach can substantially outperform several state-of-the-art unsupervised systems, and even achieves competitive performance compared to supervised methods.
%R 10.18653/v1/P19-1308
%U https://aclanthology.org/P19-1308
%U https://doi.org/10.18653/v1/P19-1308
%P 3190-3196

Markdown (Informal)

[MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction](https://aclanthology.org/P19-1308) (Yang et al., ACL 2019)

• MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction (Yang et al., ACL 2019)

ACL

• Pengcheng Yang, Fuli Luo, Peng Chen, Tianyu Liu, and Xu Sun. 2019. MAAM: A Morphology-Aware Alignment Model for Unsupervised Bilingual Lexicon Induction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3190–3196, Florence, Italy. Association for Computational Linguistics.