<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="alper-etal-2026-cwomp">
    <titleInfo>
        <title>CWoMP: Morpheme Representation Learning for Interlinear Glossing</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Morris</namePart>
        <namePart type="family">Alper</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Enora</namePart>
        <namePart type="family">Rice</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Bhargav</namePart>
        <namePart type="family">Shandilya</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Alexis</namePart>
        <namePart type="family">Palmer</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lori</namePart>
        <namePart type="family">Levin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Hung-Yi</namePart>
            <namePart type="family">Lee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sunipa</namePart>
            <namePart type="family">Dev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Desmond</namePart>
            <namePart type="family">Elliott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-459-0</identifier>
    </relatedItem>
    <abstract>Interlinear glossed text (IGT) is a standard notation for language documentation which is linguistically rich but laborious to produce manually. Recent automated IGT methods treat glosses as character sequences, neglecting their compositional structure. We propose CWoMP (Contrastive Word-Morpheme Pretraining), which instead treats morphemes as atomic form-meaning units with learned representations. A contrastively trained encoder aligns words-in-context with their constituent morphemes in a shared embedding space; an autoregressive decoder then generates the morpheme sequence by retrieving entries from a mutable lexicon of these embeddings. Predictions are interpretable–grounded in lexicon entries–and users can improve results at inference time by expanding the lexicon without retraining. We evaluate on diverse low-resource languages, showing that CWoMP outperforms existing methods while being significantly more efficient, with particularly strong gains in extremely low-resource settings.</abstract>
    <identifier type="citekey">alper-etal-2026-cwomp</identifier>
    <location>
        <url>https://aclanthology.org/2026.emnlp-main.675/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>15535</start>
            <end>15552</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T CWoMP: Morpheme Representation Learning for Interlinear Glossing
%A Alper, Morris
%A Rice, Enora
%A Shandilya, Bhargav
%A Palmer, Alexis
%A Levin, Lori
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F alper-etal-2026-cwomp
%X Interlinear glossed text (IGT) is a standard notation for language documentation which is linguistically rich but laborious to produce manually. Recent automated IGT methods treat glosses as character sequences, neglecting their compositional structure. We propose CWoMP (Contrastive Word-Morpheme Pretraining), which instead treats morphemes as atomic form-meaning units with learned representations. A contrastively trained encoder aligns words-in-context with their constituent morphemes in a shared embedding space; an autoregressive decoder then generates the morpheme sequence by retrieving entries from a mutable lexicon of these embeddings. Predictions are interpretable–grounded in lexicon entries–and users can improve results at inference time by expanding the lexicon without retraining. We evaluate on diverse low-resource languages, showing that CWoMP outperforms existing methods while being significantly more efficient, with particularly strong gains in extremely low-resource settings.
%U https://aclanthology.org/2026.emnlp-main.675/
%P 15535-15552

Markdown (Informal)

[CWoMP: Morpheme Representation Learning for Interlinear Glossing](https://aclanthology.org/2026.emnlp-main.675/) (Alper et al., EMNLP 2026)

• CWoMP: Morpheme Representation Learning for Interlinear Glossing (Alper et al., EMNLP 2026)

ACL

• Morris Alper, Enora Rice, Bhargav Shandilya, Alexis Palmer, and Lori Levin. 2026. CWoMP: Morpheme Representation Learning for Interlinear Glossing. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 15535–15552, Budapest, Hungary. Association for Computational Linguistics.