Abstract
Recent research demonstrates that word embeddings, trained on the human-generated corpus, have strong gender biases in embedding spaces, and these biases can result in the discriminative results from the various downstream tasks. Whereas the previous methods project word embeddings into a linear subspace for debiasing, we introduce a Latent Disentanglement method with a siamese autoencoder structure with an adapted gradient reversal layer. Our structure enables the separation of the semantic latent information and gender latent information of given word into the disjoint latent dimensions. Afterwards, we introduce a Counterfactual Generation to convert the gender information of words, so the original and the modified embeddings can produce a gender-neutralized word embedding after geometric alignment regularization, without loss of semantic information. From the various quantitative and qualitative debiasing experiments, our method shows to be better than existing debiasing methods in debiasing word embeddings. In addition, Our method shows the ability to preserve semantic information during debiasing by minimizing the semantic information losses for extrinsic NLP downstream tasks.
Original language | English |
---|---|
Title of host publication | Findings of the Association for Computational Linguistics Findings of ACL |
Subtitle of host publication | EMNLP 2020 |
Publisher | Association for Computational Linguistics (ACL) |
Pages | 3126-3140 |
Number of pages | 15 |
ISBN (Electronic) | 9781952148903 |
State | Published - 2020 |
Externally published | Yes |
Event | Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 - Virtual, Online Duration: 16 Nov 2020 → 20 Nov 2020 |
Publication series
Name | Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020 |
---|
Conference
Conference | Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020 |
---|---|
City | Virtual, Online |
Period | 16/11/20 → 20/11/20 |
Bibliographical note
Funding Information:This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1C1B6008652)
Publisher Copyright:
© 2020 Association for Computational Linguistics