Enhancing Bilingual Lexicon Induction via Bi-directional Translation Pair Retrieving


  • Qiuyu Ding Harbin Institute of Technology
  • Hailong Cao Harbin Institute of Technology
  • Tiejun Zhao Harbin Institute of Technology




NLP: Machine Translation, Multilinguality, Cross-Lingual NLP, ML: Representation Learning


Most Bilingual Lexicon Induction (BLI) methods retrieve word translation pairs by finding the closest target word for a given source word based on cross-lingual word embeddings (WEs). However, we find that solely retrieving translation from the source-to-target perspective leads to some false positive translation pairs, which significantly harm the precision of BLI. To address this problem, we propose a novel and effective method to improve translation pair retrieval in cross-lingual WEs. Specifically, we consider both source-side and target-side perspectives throughout the retrieval process to alleviate false positive word pairings that emanate from a single perspective. On a benchmark dataset of BLI, our proposed method achieves competitive performance compared to existing state-of-the-art (SOTA) methods. It demonstrates effectiveness and robustness across six experimental languages, including similar language pairs and distant language pairs, under both supervised and unsupervised settings.



How to Cite

Ding, Q., Cao, H., & Zhao, T. (2024). Enhancing Bilingual Lexicon Induction via Bi-directional Translation Pair Retrieving. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 17898-17906. https://doi.org/10.1609/aaai.v38i16.29744



AAAI Technical Track on Natural Language Processing I