@inproceedings{lund-etal-2023-gender,
    title = "Gender-Inclusive Grammatical Error Correction through Augmentation",
    author = "Lund, Gunnar  and
      Omelianchuk, Kostiantyn  and
      Samokhin, Igor",
    editor = {Kochmar, Ekaterina  and
      Burstein, Jill  and
      Horbach, Andrea  and
      Laarmann-Quante, Ronja  and
      Madnani, Nitin  and
      Tack, Ana{\"\i}s  and
      Yaneva, Victoria  and
      Yuan, Zheng  and
      Zesch, Torsten},
    booktitle = "Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.bea-1.13",
    doi = "10.18653/v1/2023.bea-1.13",
    pages = "148--162",
    abstract = "In this paper we show that GEC systems display gender bias related to the use of masculine and feminine terms and the gender-neutral singular {``}they{''}. We develop parallel datasets of texts with masculine and feminine terms, and singular {``}they{''}, and use them to quantify gender bias in three competitive GEC systems. We contribute a novel data augmentation technique for singular {``}they{''} leveraging linguistic insights about its distribution relative to plural {``}they{''}. We demonstrate that both this data augmentation technique and a refinement of a similar augmentation technique for masculine and feminine terms can generate training data that reduces bias in GEC systems, especially with respect to singular {``}they{''} while maintaining the same level of quality.",
}

In this paper we show that GEC systems display gender bias related to the use of masculine and feminine terms and the gender-neutral singular {``}they{''}. We develop parallel datasets of texts with masculine and feminine terms, and singular {``}they{''}, and use them to quantify gender bias in three competitive GEC systems. We contribute a novel data augmentation technique for singular {``}they{''} leveraging linguistic insights about its distribution relative to plural {``}they{''}. We demonstrate that both this data augmentation technique and a refinement of a similar augmentation technique for masculine and feminine terms can generate training data that reduces bias in GEC systems, especially with respect to singular {``}they{''} while maintaining the same level of quality.
Lund, Gunnar Omelianchuk, Kostiantyn Samokhin, Igor
Gender-Inclusive Grammatical Error Correction through Augmentation