@inproceedings{tian-etal-2022-rapo,
title = "{RAPO}: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction",
author = "Tian, Zhoujin and
Li, Chaozhuo and
Ren, Shuo and
Zuo, Zhiqiang and
Wen, Zengxuan and
Hu, Xinyue and
Han, Xiao and
Huang, Haizhen and
Deng, Denvy and
Zhang, Qi and
Xie, Xing",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.606",
doi = "10.18653/v1/2022.emnlp-main.606",
pages = "8870--8883",
abstract = "Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the distances between words in the aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates. In addition, the mapping function is globally shared by all words, whose performance might be hindered by the deviations in the distributions of different languages. In this work, we propose a novel ranking-oriented induction model RAPO to learn personalized mapping function for each word. RAPO is capable of enjoying the merits from the unique characteristics of a single word and the cross-language isomorphism simultaneously. Extensive experimental results on public datasets including both rich-resource and low-resource languages demonstrate the superiority of our proposal. Our code is publicly available in \url{https://github.com/Jlfj345wf/RAPO}.",
}
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<abstract>Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the distances between words in the aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates. In addition, the mapping function is globally shared by all words, whose performance might be hindered by the deviations in the distributions of different languages. In this work, we propose a novel ranking-oriented induction model RAPO to learn personalized mapping function for each word. RAPO is capable of enjoying the merits from the unique characteristics of a single word and the cross-language isomorphism simultaneously. Extensive experimental results on public datasets including both rich-resource and low-resource languages demonstrate the superiority of our proposal. Our code is publicly available in https://github.com/Jlfj345wf/RAPO.</abstract>
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%0 Conference Proceedings
%T RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
%A Tian, Zhoujin
%A Li, Chaozhuo
%A Ren, Shuo
%A Zuo, Zhiqiang
%A Wen, Zengxuan
%A Hu, Xinyue
%A Han, Xiao
%A Huang, Haizhen
%A Deng, Denvy
%A Zhang, Qi
%A Xie, Xing
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F tian-etal-2022-rapo
%X Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the distances between words in the aligned pairs, while suffering from low discriminative capability to distinguish the relative orders between positive and negative candidates. In addition, the mapping function is globally shared by all words, whose performance might be hindered by the deviations in the distributions of different languages. In this work, we propose a novel ranking-oriented induction model RAPO to learn personalized mapping function for each word. RAPO is capable of enjoying the merits from the unique characteristics of a single word and the cross-language isomorphism simultaneously. Extensive experimental results on public datasets including both rich-resource and low-resource languages demonstrate the superiority of our proposal. Our code is publicly available in https://github.com/Jlfj345wf/RAPO.
%R 10.18653/v1/2022.emnlp-main.606
%U https://aclanthology.org/2022.emnlp-main.606
%U https://doi.org/10.18653/v1/2022.emnlp-main.606
%P 8870-8883
Markdown (Informal)
[RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction](https://aclanthology.org/2022.emnlp-main.606) (Tian et al., EMNLP 2022)
ACL
- Zhoujin Tian, Chaozhuo Li, Shuo Ren, Zhiqiang Zuo, Zengxuan Wen, Xinyue Hu, Xiao Han, Haizhen Huang, Denvy Deng, Qi Zhang, and Xing Xie. 2022. RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 8870–8883, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.