Cross-Domain Bilingual Lexicon Induction via Pretrained Language Models

• URL: http://arxiv.org/abs/2505.23146v1
• Date: Thu, 29 May 2025 06:37:02 GMT
• Title: Cross-Domain Bilingual Lexicon Induction via Pretrained Language Models
• Authors: Qiuyu Ding, Zhiqiang Cao, Hailong Cao, Tiejun Zhao,
• Abstract summary: We propose a new task of BLI, which is to use the monolingual corpus of the general domain and target domain to extract domain-specific bilingual dictionaries.<n>Motivated by the ability of Pre-trained models, we propose a method to get better word embeddings that build on the recent work on BLI.<n> Experimental results show that our method can improve performances over robust BLI baselines on three specific domains by averagely improving 0.78 points.
• Score: 22.297388572921477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bilingual Lexicon Induction (BLI) is generally based on common domain data to obtain monolingual word embedding, and by aligning the monolingual word embeddings to obtain the cross-lingual embeddings which are used to get the word translation pairs. In this paper, we propose a new task of BLI, which is to use the monolingual corpus of the general domain and target domain to extract domain-specific bilingual dictionaries. Motivated by the ability of Pre-trained models, we propose a method to get better word embeddings that build on the recent work on BLI. This way, we introduce the Code Switch(Qin et al., 2020) firstly in the cross-domain BLI task, which can match differit is yet to be seen whether these methods are suitable for bilingual lexicon extraction in professional fields. As we can see in table 1, the classic and efficient BLI approach, Muse and Vecmap, perform much worse on the Medical dataset than on the Wiki dataset. On one hand, the specialized domain data set is relatively smaller compared to the generic domain data set generally, and specialized words have a lower frequency, which will directly affect the translation quality of bilingual dictionaries. On the other hand, static word embeddings are widely used for BLI, however, in some specific fields, the meaning of words is greatly influenced by context, in this case, using only static word embeddings may lead to greater bias. ent strategies in different contexts, making the model more suitable for this task. Experimental results show that our method can improve performances over robust BLI baselines on three specific domains by averagely improving 0.78 points.

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