TY - GEN
T1 - How does grammatical gender affect noun representations in gender-marking languages?
AU - Gonen, Hila
AU - Kementchedjhieva, Yova
AU - Goldberg, Yoav
N1 - Publisher Copyright: © 2019 Association for Computational Linguistics.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Many natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun's gender. We show that this affects the word representations of inanimate nouns, resulting in nouns with the same gender being closer to each other than nouns with different gender. While "embedding de-biasing" methods fail to remove the effect, we demonstrate that a careful application of methods that neutralize grammatical gender signals from the words' context when training word embeddings is effective in removing it. Fixing the grammatical gender bias yields a positive effect on the quality of the resulting word embeddings, both in monolingual and cross-lingual settings. We note that successfully removing gender signals, while achievable, is not trivial to do and that a language-specific morphological analyzer, together with careful usage of it, are essential for achieving good results.
AB - Many natural languages assign grammatical gender also to inanimate nouns in the language. In such languages, words that relate to the gender-marked nouns are inflected to agree with the noun's gender. We show that this affects the word representations of inanimate nouns, resulting in nouns with the same gender being closer to each other than nouns with different gender. While "embedding de-biasing" methods fail to remove the effect, we demonstrate that a careful application of methods that neutralize grammatical gender signals from the words' context when training word embeddings is effective in removing it. Fixing the grammatical gender bias yields a positive effect on the quality of the resulting word embeddings, both in monolingual and cross-lingual settings. We note that successfully removing gender signals, while achievable, is not trivial to do and that a language-specific morphological analyzer, together with careful usage of it, are essential for achieving good results.
UR - http://www.scopus.com/inward/record.url?scp=85084330092&partnerID=8YFLogxK
M3 - Conference contribution
T3 - CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference
SP - 463
EP - 471
BT - CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference
T2 - 23rd Conference on Computational Natural Language Learning, CoNLL 2019
Y2 - 3 November 2019 through 4 November 2019
ER -