Unsupervised Bilingual Lexicon Induction for Low Resource Languages
This work addresses the lack of bilingual lexicons for low-resource languages, but it is incremental as it combines existing methods rather than introducing new ones.
The paper tackled the problem of unsupervised bilingual lexicon induction for low-resource languages by testing combinations of existing techniques within the VecMap framework, resulting in identified best combinations and released dictionaries for English-Sinhala and English-Punjabi.
Bilingual lexicons play a crucial role in various Natural Language Processing tasks. However, many low-resource languages (LRLs) do not have such lexicons, and due to the same reason, cannot benefit from the supervised Bilingual Lexicon Induction (BLI) techniques. To address this, unsupervised BLI (UBLI) techniques were introduced. A prominent technique in this line is structure-based UBLI. It is an iterative method, where a seed lexicon, which is initially learned from monolingual embeddings is iteratively improved. There have been numerous improvements to this core idea, however they have been experimented with independently of each other. In this paper, we investigate whether using these techniques simultaneously would lead to equal gains. We use the unsupervised version of VecMap, a commonly used structure-based UBLI framework, and carry out a comprehensive set of experiments using the LRL pairs, English-Sinhala, English-Tamil, and English-Punjabi. These experiments helped us to identify the best combination of the extensions. We also release bilingual dictionaries for English-Sinhala and English-Punjabi.