Name
Abstract | ||
|---|---|---|
ABSTRACTIn this paper, we advance the current state-of-the-art method for debiasing monolingual word embeddings so as to generalize well in a multilingual setting. We consider different methods to quantify bias and different debiasing approaches for monolingual as well as multilingual settings. We demonstrate the significance of our bias-mitigation approach on downstream NLP applications. Our proposed methods establish the state-of-the-art performance for debiasing multilingual embeddings for three Indian languages - Hindi, Bengali, and Telugu in addition to English. We believe that our work will open up new opportunities in building unbiased downstream NLP applications that are inherently dependent on the quality of the word embeddings used. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3465336.3475118 | Hypertext and Hypermedia |
Keywords | DocType | Citations |
Debiasing multilingual embeddings, Gender debias, Debiasing Indian languages | Conference | 0 |
PageRank | References | Authors |
0.34 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Srijan Bansal | 1 | 1 | 1.03 |
| Vishal Garimella | 2 | 0 | 0.34 |
| Ayush Suhane | 3 | 0 | 0.34 |
| Animesh Mukherjee | 4 | 392 | 62.78 |