Gender bias in multilingual neural machine translation: The architecture matters
Multilingual Neural Machine Translation architectures mainly differ in the amount of sharing modules and parameters among languages. In this paper, and from an algorithmic perspective, we explore if the chosen architecture, when trained with the same data, influences the gender bias accuracy. Experi...
| Autores: | , , , , , |
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| Tipo de recurso: | informe técnico |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/348079 |
| Acceso en línea: | https://hdl.handle.net/2117/348079 |
| Access Level: | acceso abierto |
| Palabra clave: | Sexism in language Machine translating Natural language processing (Computer science) Sexisme en el llenguatge Traducció automàtica Tractament del llenguatge natural (Informàtica) Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic |
| Sumario: | Multilingual Neural Machine Translation architectures mainly differ in the amount of sharing modules and parameters among languages. In this paper, and from an algorithmic perspective, we explore if the chosen architecture, when trained with the same data, influences the gender bias accuracy. Experiments in four language pairs show that Language-Specific encoders-decoders exhibit less bias than the Shared encoder-decoder architecture. Further interpretability analysis of source embeddings and the attention shows that, in the LanguageSpecific case, the embeddings encode more gender information, and its attention is more diverted. Both behaviors help in mitigating gender bias. |
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