Topics to Avoid: Demoting Latent Confounds in Text Classification

Sachin Kumar, Shuly Wintner, Noah A. Smith, Yulia Tsvetkov

Abstract
Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well. In this work, we observe this limitation with respect to the task of native language identification. We find that standard text classifiers which perform well on the test set end up learning topical features which are confounds of the prediction task (e.g., if the input text mentions Sweden, the classifier predicts that the author’s native language is Swedish). We propose a method that represents the latent topical confounds and a model which “unlearns” confounding features by predicting both the label of the input text and the confound; but we train the two predictors adversarially in an alternating fashion to learn a text representation that predicts the correct label but is less prone to using information about the confound. We show that this model generalizes better and learns features that are indicative of the writing style rather than the content.
Anthology ID:
D19-1425
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:
4153–4163
Language:
URL:
https://aclanthology.org/D19-1425
DOI:
10.18653/v1/D19-1425
Bibkey:
Cite (ACL):
Sachin Kumar, Shuly Wintner, Noah A. Smith, and Yulia Tsvetkov. 2019. Topics to Avoid: Demoting Latent Confounds in Text Classification. 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 4153–4163, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Topics to Avoid: Demoting Latent Confounds in Text Classification (Kumar et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/landing_page/D19-1425.pdf
Code
 Sachin19/adversarial-classify
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