Dual-perspective fusion for word translation enhancement

Abstract

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 apply a fusion of both source-side and target-side perspectives throughout the retrieval process to alleviate false positive word pairings that emanate from a single perspective. Moreover, in translation scenarios using Large Language Models (LLMs), we propose fusing the LLMs perspective with the BLI model perspective to enhance LLM’s translation capability. On benchmark datasets 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.