@inproceedings{li-etal-2022-improving-bilingual,
title = "Improving Bilingual Lexicon Induction with Cross-Encoder Reranking",
author = "Li, Yaoyiran and
Liu, Fangyu and
Vuli{\'c}, Ivan and
Korhonen, Anna",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.302",
doi = "10.18653/v1/2022.findings-emnlp.302",
pages = "4100--4116",
abstract = "Bilingual lexicon induction (BLI) with limited bilingual supervision is a crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI methods rely on the induction of cross-lingual word embeddings (CLWEs) to capture cross-lingual word similarities; such CLWEs are obtained {\textless}b{\textgreater}1){\textless}/b{\textgreater} via traditional static models (e.g., VecMap), or {\textless}b{\textgreater}2){\textless}/b{\textgreater} by extracting type-level CLWEs from multilingual pretrained language models (mPLMs), or {\textless}b{\textgreater}3){\textless}/b{\textgreater} through combining the former two options. In this work, we propose a novel semi-supervised {\textless}i{\textgreater}post-hoc{\textless}/i{\textgreater} reranking method termed {\textless}b{\textgreater}BLICEr{\textless}/b{\textgreater} ({\textless}b{\textgreater}BLI{\textless}/b{\textgreater} with {\textless}b{\textgreater}C{\textless}/b{\textgreater}ross-{\textless}b{\textgreater}E{\textless}/b{\textgreater}ncoder {\textless}b{\textgreater}R{\textless}/b{\textgreater}eranking), applicable to any precalculated CLWE space, which improves their BLI capability. The key idea is to {`}extract{'} cross-lingual lexical knowledge from mPLMs, and then combine it with the original CLWEs. This crucial step is done via {\textless}b{\textgreater}1){\textless}/b{\textgreater} creating a word similarity dataset, comprising positive word pairs (i.e., true translations) and hard negative pairs induced from the original CLWE space, and then {\textless}b{\textgreater}2){\textless}/b{\textgreater} fine-tuning an mPLM (e.g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores. At inference, we {\textless}b{\textgreater}3){\textless}/b{\textgreater} combine the similarity score from the original CLWE space with the score from the BLI-tuned cross-encoder. BLICEr establishes new state-of-the-art results on two standard BLI benchmarks spanning a wide spectrum of diverse languages: it substantially outperforms a series of strong baselines across the board. We also validate the robustness of BLICEr with different CLWEs.",
}
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<abstract>Bilingual lexicon induction (BLI) with limited bilingual supervision is a crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI methods rely on the induction of cross-lingual word embeddings (CLWEs) to capture cross-lingual word similarities; such CLWEs are obtained \textlessb\textgreater1)\textless/b\textgreater via traditional static models (e.g., VecMap), or \textlessb\textgreater2)\textless/b\textgreater by extracting type-level CLWEs from multilingual pretrained language models (mPLMs), or \textlessb\textgreater3)\textless/b\textgreater through combining the former two options. In this work, we propose a novel semi-supervised \textlessi\textgreaterpost-hoc\textless/i\textgreater reranking method termed \textlessb\textgreaterBLICEr\textless/b\textgreater (\textlessb\textgreaterBLI\textless/b\textgreater with \textlessb\textgreaterC\textless/b\textgreaterross-\textlessb\textgreaterE\textless/b\textgreaterncoder \textlessb\textgreaterR\textless/b\textgreatereranking), applicable to any precalculated CLWE space, which improves their BLI capability. The key idea is to ‘extract’ cross-lingual lexical knowledge from mPLMs, and then combine it with the original CLWEs. This crucial step is done via \textlessb\textgreater1)\textless/b\textgreater creating a word similarity dataset, comprising positive word pairs (i.e., true translations) and hard negative pairs induced from the original CLWE space, and then \textlessb\textgreater2)\textless/b\textgreater fine-tuning an mPLM (e.g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores. At inference, we \textlessb\textgreater3)\textless/b\textgreater combine the similarity score from the original CLWE space with the score from the BLI-tuned cross-encoder. BLICEr establishes new state-of-the-art results on two standard BLI benchmarks spanning a wide spectrum of diverse languages: it substantially outperforms a series of strong baselines across the board. We also validate the robustness of BLICEr with different CLWEs.</abstract>
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%0 Conference Proceedings
%T Improving Bilingual Lexicon Induction with Cross-Encoder Reranking
%A Li, Yaoyiran
%A Liu, Fangyu
%A Vulić, Ivan
%A Korhonen, Anna
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F li-etal-2022-improving-bilingual
%X Bilingual lexicon induction (BLI) with limited bilingual supervision is a crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI methods rely on the induction of cross-lingual word embeddings (CLWEs) to capture cross-lingual word similarities; such CLWEs are obtained \textlessb\textgreater1)\textless/b\textgreater via traditional static models (e.g., VecMap), or \textlessb\textgreater2)\textless/b\textgreater by extracting type-level CLWEs from multilingual pretrained language models (mPLMs), or \textlessb\textgreater3)\textless/b\textgreater through combining the former two options. In this work, we propose a novel semi-supervised \textlessi\textgreaterpost-hoc\textless/i\textgreater reranking method termed \textlessb\textgreaterBLICEr\textless/b\textgreater (\textlessb\textgreaterBLI\textless/b\textgreater with \textlessb\textgreaterC\textless/b\textgreaterross-\textlessb\textgreaterE\textless/b\textgreaterncoder \textlessb\textgreaterR\textless/b\textgreatereranking), applicable to any precalculated CLWE space, which improves their BLI capability. The key idea is to ‘extract’ cross-lingual lexical knowledge from mPLMs, and then combine it with the original CLWEs. This crucial step is done via \textlessb\textgreater1)\textless/b\textgreater creating a word similarity dataset, comprising positive word pairs (i.e., true translations) and hard negative pairs induced from the original CLWE space, and then \textlessb\textgreater2)\textless/b\textgreater fine-tuning an mPLM (e.g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores. At inference, we \textlessb\textgreater3)\textless/b\textgreater combine the similarity score from the original CLWE space with the score from the BLI-tuned cross-encoder. BLICEr establishes new state-of-the-art results on two standard BLI benchmarks spanning a wide spectrum of diverse languages: it substantially outperforms a series of strong baselines across the board. We also validate the robustness of BLICEr with different CLWEs.
%R 10.18653/v1/2022.findings-emnlp.302
%U https://aclanthology.org/2022.findings-emnlp.302
%U https://doi.org/10.18653/v1/2022.findings-emnlp.302
%P 4100-4116
Markdown (Informal)
[Improving Bilingual Lexicon Induction with Cross-Encoder Reranking](https://aclanthology.org/2022.findings-emnlp.302) (Li et al., Findings 2022)
ACL