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
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4100–4116
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.302
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.findings-emnlp.302.pdf