<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="urbizu-etal-2019-deep">
    <titleInfo>
        <title>Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Gorka</namePart>
        <namePart type="family">Urbizu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ander</namePart>
        <namePart type="family">Soraluze</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Olatz</namePart>
        <namePart type="family">Arregi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Maciej</namePart>
            <namePart type="family">Ogrodniczuk</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sameer</namePart>
            <namePart type="family">Pradhan</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yulia</namePart>
            <namePart type="family">Grishina</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Vincent</namePart>
            <namePart type="family">Ng</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Minneapolis, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we present a cross-lingual neural coreference resolution system for a less-resourced language such as Basque. To begin with, we build the first neural coreference resolution system for Basque, training it with the relatively small EPEC-KORREF corpus (45,000 words). Next, a cross-lingual coreference resolution system is designed. With this approach, the system learns from a bigger English corpus, using cross-lingual embeddings, to perform the coreference resolution for Basque. The cross-lingual system obtains slightly better results (40.93 F1 CoNLL) than the monolingual system (39.12 F1 CoNLL), without using any Basque language corpus to train it.</abstract>
    <identifier type="citekey">urbizu-etal-2019-deep</identifier>
    <identifier type="doi">10.18653/v1/W19-2806</identifier>
    <location>
        <url>https://aclanthology.org/W19-2806</url>
    </location>
    <part>
        <date>2019-06</date>
        <extent unit="page">
            <start>35</start>
            <end>41</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque
%A Urbizu, Gorka
%A Soraluze, Ander
%A Arregi, Olatz
%Y Ogrodniczuk, Maciej
%Y Pradhan, Sameer
%Y Grishina, Yulia
%Y Ng, Vincent
%S Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, USA
%F urbizu-etal-2019-deep
%X In this paper, we present a cross-lingual neural coreference resolution system for a less-resourced language such as Basque. To begin with, we build the first neural coreference resolution system for Basque, training it with the relatively small EPEC-KORREF corpus (45,000 words). Next, a cross-lingual coreference resolution system is designed. With this approach, the system learns from a bigger English corpus, using cross-lingual embeddings, to perform the coreference resolution for Basque. The cross-lingual system obtains slightly better results (40.93 F1 CoNLL) than the monolingual system (39.12 F1 CoNLL), without using any Basque language corpus to train it.
%R 10.18653/v1/W19-2806
%U https://aclanthology.org/W19-2806
%U https://doi.org/10.18653/v1/W19-2806
%P 35-41

Markdown (Informal)

[Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque](https://aclanthology.org/W19-2806) (Urbizu et al., CRAC 2019)

• Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque (Urbizu et al., CRAC 2019)

ACL

• Gorka Urbizu, Ander Soraluze, and Olatz Arregi. 2019. Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque. In Proceedings of the Second Workshop on Computational Models of Reference, Anaphora and Coreference, pages 35–41, Minneapolis, USA. Association for Computational Linguistics.