Interpreting Gender Bias in Neural Machine Translation: Multilingual Architecture Matters

Authors

  • Marta R. Costa-jussà TALP Research Center, Universitat Politècnica de Catalunya, Barcelona
  • Carlos Escolano TALP Research Center, Universitat Politècnica de Catalunya, Barcelona
  • Christine Basta TALP Research Center, Universitat Politècnica de Catalunya, Barcelona Institute of Graduate Studies and Research, Alexandria University, Egypt
  • Javier Ferrando TALP Research Center, Universitat Politècnica de Catalunya, Barcelona
  • Roser Batlle TALP Research Center, Universitat Politècnica de Catalunya, Barcelona
  • Ksenia Kharitonova TALP Research Center, Universitat Politècnica de Catalunya, Barcelona

DOI:

https://doi.org/10.1609/aaai.v36i11.21442

Keywords:

AI For Social Impact (AISI Track Papers Only)

Abstract

Multilingual neural machine translation architectures mainly differ in the number of sharing modules and parameters applied among languages. In this paper, and from an algorithmic perspective, we explore whether the chosen architecture, when trained with the same data, influences the level of gender bias. Experiments conducted in three language pairs show that language-specific encoder-decoders exhibit less bias than the shared architecture. We propose two methods for interpreting and studying gender bias in machine translation based on source embeddings and attention. Our analysis shows that, in the language-specific case, the embeddings encode more gender information, and their attention is more diverted. Both behaviors help in mitigating gender bias.

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Published

2022-06-28

How to Cite

Costa-jussà, M. R., Escolano, C., Basta, C., Ferrando, J., Batlle, R., & Kharitonova, K. (2022). Interpreting Gender Bias in Neural Machine Translation: Multilingual Architecture Matters. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 11855-11863. https://doi.org/10.1609/aaai.v36i11.21442