A topic model outputs a set of multinomial distributions over words for each topic. In this paper, we investigate the value of bilingual topic models, i.e., a bilingual Latent Dirichlet Allocation model for finding translations of terms in comparable corpora without using any linguistic resources. Experiments on a document-aligned English-Italian Wikipedia corpus confirm that the developed methods which only use knowledge from word-topic distributions outperform methods based on similarity measures in the original word-document space. The best results, obtained by combining knowledge from wordtopic distributions with similarity measures in the original space, are also reported.
REF investigate the algorithm of bilingual topic models, which finds translations of terms in comparable corpora by using knowledge from word-topic distributions.
Identifying Word Translations from Comparable Corpora Using Latent Topic Models
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Computer Science