@inproceedings{basta-etal-2020-towards,
    title = "Towards Mitigating Gender Bias in a decoder-based Neural Machine Translation model by Adding Contextual Information",
    author = "Basta, Christine  and
      Costa-juss{\`a}, Marta R.  and
      Fonollosa, Jos{\'e} A. R.",
    editor = "Cunha, Rossana  and
      Shaikh, Samira  and
      Varis, Erika  and
      Georgi, Ryan  and
      Tsai, Alicia  and
      Anastasopoulos, Antonios  and
      Chandu, Khyathi Raghavi",
    booktitle = "Proceedings of the Fourth Widening Natural Language Processing Workshop",
    month = jul,
    year = "2020",
    address = "Seattle, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.winlp-1.25/",
    doi = "10.18653/v1/2020.winlp-1.25",
    pages = "99--102",
    abstract = "Gender bias negatively impacts many natural language processing applications, including machine translation (MT). The motivation behind this work is to study whether recent proposed MT techniques are significantly contributing to attenuate biases in document-level and gender-balanced data. For the study, we consider approaches of adding the previous sentence and the speaker information, implemented in a decoder-based neural MT system. We show improvements both in translation quality (+1 BLEU point) as well as in gender bias mitigation on WinoMT (+5{\%} accuracy)."
}