Improving Bilingual Lexicon Induction with Cross-Encoder Reranking

Yaoyiran Li, Fangyu Liu, Ivan Vulić, Anna Korhonen


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 <b>1)</b> via traditional static models (e.g., VecMap), or <b>2)</b> by extracting type-level CLWEs from multilingual pretrained language models (mPLMs), or <b>3)</b> through combining the former two options. In this work, we propose a novel semi-supervised <i>post-hoc</i> reranking method termed <b>BLICEr</b> (<b>BLI</b> with <b>C</b>ross-<b>E</b>ncoder <b>R</b>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 <b>1)</b> 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 <b>2)</b> fine-tuning an mPLM (e.g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores. At inference, we <b>3)</b> 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.
Anthology ID:
2022.findings-emnlp.302
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4100–4116
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.302
DOI:
10.18653/v1/2022.findings-emnlp.302
Bibkey:
Cite (ACL):
Yaoyiran Li, Fangyu Liu, Ivan Vulić, and Anna Korhonen. 2022. Improving Bilingual Lexicon Induction with Cross-Encoder Reranking. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 4100–4116, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking (Li et al., Findings 2022)
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PDF:
https://aclanthology.org/2022.findings-emnlp.302.pdf
Video:
 https://aclanthology.org/2022.findings-emnlp.302.mp4