Detecting Highly Confident Word Translations from Comparable Corpora without Any Prior Knowledge

In this paper, we extend the work on using latent cross-language topic models for identifying word translations across comparable corpora. We present a novel precisionoriented algorithm that relies on per-topic word distributions obtained by the bilingual LDA (BiLDA) latent topic model. The algorithm aims at harvesting only the most probable word translations across languages in a greedy fashion, without any prior knowledge about the language pair, relying on a symmetrization process and the one-to-one constraint. We report our results for Italian-English and Dutch-English language pairs that outperform the current state-of-the-art results by a significant margin. In addition, we show how to use the algorithm for the construction of high-quality initial seed lexicons of translations.