@inproceedings{sahai-sharma-2021-predicting,
title = "Predicting and Explaining {F}rench Grammatical Gender",
author = "Sahai, Saumya and
Sharma, Dravyansh",
editor = {Vylomova, Ekaterina and
Salesky, Elizabeth and
Mielke, Sabrina and
Lapesa, Gabriella and
Kumar, Ritesh and
Hammarstr{\"o}m, Harald and
Vuli{\'c}, Ivan and
Korhonen, Anna and
Reichart, Roi and
Ponti, Edoardo Maria and
Cotterell, Ryan},
booktitle = "Proceedings of the Third Workshop on Computational Typology and Multilingual NLP",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.sigtyp-1.9",
doi = "10.18653/v1/2021.sigtyp-1.9",
pages = "90--96",
abstract = "Grammatical gender may be determined by semantics, orthography, phonology, or could even be arbitrary. Identifying patterns in the factors that govern noun genders can be useful for language learners, and for understanding innate linguistic sources of gender bias. Traditional manual rule-based approaches may be substituted by more accurate and scalable but harder-to-interpret computational approaches for predicting gender from typological information. In this work, we propose interpretable gender classification models for French, which obtain the best of both worlds. We present high accuracy neural approaches which are augmented by a novel global surrogate based approach for explaining predictions. We introduce {`}auxiliary attributes{'} to provide tunable explanation complexity.",
}
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%0 Conference Proceedings
%T Predicting and Explaining French Grammatical Gender
%A Sahai, Saumya
%A Sharma, Dravyansh
%Y Vylomova, Ekaterina
%Y Salesky, Elizabeth
%Y Mielke, Sabrina
%Y Lapesa, Gabriella
%Y Kumar, Ritesh
%Y Hammarström, Harald
%Y Vulić, Ivan
%Y Korhonen, Anna
%Y Reichart, Roi
%Y Ponti, Edoardo Maria
%Y Cotterell, Ryan
%S Proceedings of the Third Workshop on Computational Typology and Multilingual NLP
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F sahai-sharma-2021-predicting
%X Grammatical gender may be determined by semantics, orthography, phonology, or could even be arbitrary. Identifying patterns in the factors that govern noun genders can be useful for language learners, and for understanding innate linguistic sources of gender bias. Traditional manual rule-based approaches may be substituted by more accurate and scalable but harder-to-interpret computational approaches for predicting gender from typological information. In this work, we propose interpretable gender classification models for French, which obtain the best of both worlds. We present high accuracy neural approaches which are augmented by a novel global surrogate based approach for explaining predictions. We introduce ‘auxiliary attributes’ to provide tunable explanation complexity.
%R 10.18653/v1/2021.sigtyp-1.9
%U https://aclanthology.org/2021.sigtyp-1.9
%U https://doi.org/10.18653/v1/2021.sigtyp-1.9
%P 90-96
Markdown (Informal)
[Predicting and Explaining French Grammatical Gender](https://aclanthology.org/2021.sigtyp-1.9) (Sahai & Sharma, SIGTYP 2021)
ACL