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        <title>A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability</title>
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    <name type="personal">
        <namePart type="given">Weiwei</namePart>
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            <namePart type="given">Jing</namePart>
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    <abstract>Multilingual topic models (MTMs) learn topics on documents in multiple languages. Past models align topics across languages by implicitly assuming the documents in different languages are highly comparable, often a false assumption. We introduce a new model that does not rely on this assumption, particularly useful in important low-resource language scenarios. Our MTM learns weighted topic links and connects cross-lingual topics only when the dominant words defining them are similar, outperforming LDA and previous MTMs in classification tasks using documents’ topic posteriors as features. It also learns coherent topics on documents with low comparability.</abstract>
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    <identifier type="doi">10.18653/v1/D19-1120</identifier>
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%0 Conference Proceedings
%T A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability
%A Yang, Weiwei
%A Boyd-Graber, Jordan
%A Resnik, Philip
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F yang-etal-2019-multilingual
%X Multilingual topic models (MTMs) learn topics on documents in multiple languages. Past models align topics across languages by implicitly assuming the documents in different languages are highly comparable, often a false assumption. We introduce a new model that does not rely on this assumption, particularly useful in important low-resource language scenarios. Our MTM learns weighted topic links and connects cross-lingual topics only when the dominant words defining them are similar, outperforming LDA and previous MTMs in classification tasks using documents’ topic posteriors as features. It also learns coherent topics on documents with low comparability.
%R 10.18653/v1/D19-1120
%U https://aclanthology.org/D19-1120
%U https://doi.org/10.18653/v1/D19-1120
%P 1243-1248

Markdown (Informal)

[A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability](https://aclanthology.org/D19-1120) (Yang et al., EMNLP-IJCNLP 2019)

• A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability (Yang et al., EMNLP-IJCNLP 2019)

ACL

• Weiwei Yang, Jordan Boyd-Graber, and Philip Resnik. 2019. A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1243–1248, Hong Kong, China. Association for Computational Linguistics.