@inproceedings{vanmassenhove-monti-2021-gender,
title = "g{EN}der-{IT}: An Annotated {E}nglish-{I}talian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomena",
author = "Vanmassenhove, Eva and
Monti, Johanna",
booktitle = "Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.gebnlp-1.1",
doi = "10.18653/v1/2021.gebnlp-1.1",
pages = "1--7",
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.",
}
Markdown (Informal)
[gENder-IT: An Annotated English-Italian Parallel Challenge Set for Cross-Linguistic Natural Gender Phenomena](https://aclanthology.org/2021.gebnlp-1.1) (Vanmassenhove & Monti, GeBNLP 2021)
ACL