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    <abstract>Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces. We propose a technique to quantitatively estimate this assumption of the isometry between two embedding spaces and empirically show that this assumption weakens as the languages in question become increasingly etymologically distant. We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) — a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique. Our proposed method obtains state of the art results on 15 of 18 language pairs on the MUSE dataset, and does particularly well when the embedding spaces don’t appear to be isometric. In addition, we also show that adding supervision stabilizes the learning procedure, and is effective even with minimal supervision.</abstract>
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%0 Conference Proceedings
%T Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
%A Patra, Barun
%A Moniz, Joel Ruben Antony
%A Garg, Sarthak
%A Gormley, Matthew R.
%A Neubig, Graham
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluí s.
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F patra-etal-2019-bilingual
%X Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces. We propose a technique to quantitatively estimate this assumption of the isometry between two embedding spaces and empirically show that this assumption weakens as the languages in question become increasingly etymologically distant. We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) — a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique. Our proposed method obtains state of the art results on 15 of 18 language pairs on the MUSE dataset, and does particularly well when the embedding spaces don’t appear to be isometric. In addition, we also show that adding supervision stabilizes the learning procedure, and is effective even with minimal supervision.
%R 10.18653/v1/P19-1018
%U https://aclanthology.org/P19-1018/
%U https://doi.org/10.18653/v1/P19-1018
%P 184-193

Markdown (Informal)

[Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces](https://aclanthology.org/P19-1018/) (Patra et al., ACL 2019)

• Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces (Patra et al., ACL 2019)

ACL

• Barun Patra, Joel Ruben Antony Moniz, Sarthak Garg, Matthew R. Gormley, and Graham Neubig. 2019. Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 184–193, Florence, Italy. Association for Computational Linguistics.