Abstract
Languages differ in terms of the absence or presence of gender features, the number of gender classes and whether and where gender features are explicitly marked. These cross-linguistic differences can lead to ambiguities that are difficult to resolve, especially for sentence-level MT systems. The identification of ambiguity and its subsequent resolution is a challenging task for which currently there aren’t any specific resources or challenge sets available. In this paper, we introduce gENder-IT, an English–Italian challenge set focusing on the resolution of natural gender phenomena by providing word-level gender tags on the English source side and multiple gender alternative translations, where needed, on the Italian target side.- Anthology ID:
- 2021.gebnlp-1.1
- Volume:
- Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- GeBNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1–7
- Language:
- URL:
- https://aclanthology.org/2021.gebnlp-1.1
- DOI:
- 10.18653/v1/2021.gebnlp-1.1
- Cite (ACL):
- Eva Vanmassenhove and Johanna Monti. 2021. gENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomena. In Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing, pages 1–7, Online. Association for Computational Linguistics.
- Cite (Informal):
- gENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomena (Vanmassenhove & Monti, GeBNLP 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.gebnlp-1.1.pdf
- Data
- gENder-IT