Enhancing Bilingual Lexicon Induction via Bi-directional Translation Pair Retrieving

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.

Authors

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

Keywords

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