<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hamalainen-etal-2021-neural">
    <titleInfo>
        <title>Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Mika</namePart>
        <namePart type="family">Hämäläinen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Niko</namePart>
        <namePart type="family">Partanen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jack</namePart>
        <namePart type="family">Rueter</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Khalid</namePart>
        <namePart type="family">Alnajjar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-may 31–2 jun</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Dobnik</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Lilja</namePart>
            <namePart type="family">Øvrelid</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Linköping University Electronic Press, Sweden</publisher>
            <place>
                <placeTerm type="text">Reykjavik, Iceland (Online)</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially large amount of training data from FSTs for 22 languages, out of which 17 are endangered. The neural models follow the same tagset as the FSTs in order to make it possible to use them as fallback systems together with the FSTs. The source code, models and datasets have been released on Zenodo.</abstract>
    <identifier type="citekey">hamalainen-etal-2021-neural</identifier>
    <location>
        <url>https://aclanthology.org/2021.nodalida-main.17</url>
    </location>
    <part>
        <date>2021-may 31–2 jun</date>
        <extent unit="page">
            <start>166</start>
            <end>177</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered
%A Hämäläinen, Mika
%A Partanen, Niko
%A Rueter, Jack
%A Alnajjar, Khalid
%Y Dobnik, Simon
%Y Øvrelid, Lilja
%S Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)
%D 2021
%8 may 31–2 jun
%I Linköping University Electronic Press, Sweden
%C Reykjavik, Iceland (Online)
%F hamalainen-etal-2021-neural
%X We train neural models for morphological analysis, generation and lemmatization for morphologically rich languages. We present a method for automatically extracting substantially large amount of training data from FSTs for 22 languages, out of which 17 are endangered. The neural models follow the same tagset as the FSTs in order to make it possible to use them as fallback systems together with the FSTs. The source code, models and datasets have been released on Zenodo.
%U https://aclanthology.org/2021.nodalida-main.17
%P 166-177

Markdown (Informal)

[Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered](https://aclanthology.org/2021.nodalida-main.17) (Hämäläinen et al., NoDaLiDa 2021)

• Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered (Hämäläinen et al., NoDaLiDa 2021)

ACL

• Mika Hämäläinen, Niko Partanen, Jack Rueter, and Khalid Alnajjar. 2021. Neural Morphology Dataset and Models for Multiple Languages, from the Large to the Endangered. In Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa), pages 166–177, Reykjavik, Iceland (Online). Linköping University Electronic Press, Sweden.