A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability

Weiwei Yang, Jordan Boyd-Graber, Philip Resnik


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.
Anthology ID:
D19-1120
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1243–1248
Language:
URL:
https://aclanthology.org/D19-1120
DOI:
10.18653/v1/D19-1120
Bibkey:
Cite (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.
Cite (Informal):
A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability (Yang et al., EMNLP-IJCNLP 2019)
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PDF:
https://aclanthology.org/D19-1120.pdf
Attachment:
 D19-1120.Attachment.pdf