Evaluating a Pivot-Based Approach for Bilingual Lexicon Extraction

Abstract

A pivot-based approach for bilingual lexicon extraction is based on the similarity of context vectors represented by words in a pivot language like English. In this paper, in order to show validity and usability of the pivot-based approach, we evaluate the approach in company with two different methods for estimating context vectors: one estimates them from two parallel corpora based on word association between source words (resp., target words) and pivot words and the other estimates them from two parallel corpora based on word alignment tools for statistical machine translation. Empirical results on two language pairs (e.g., Korean-Spanish and Korean-French) have shown that the pivot-based approach is very promising for resource-poor languages and this approach observes its validity and usability. Furthermore, for words with low frequency, our method is also well performed.

Attempting to Bypass Alignment from Comparable Corpora via Pivot Language

“…Nevertheless, the idea of involving a pivot language for translation tasks is not recent. Bilingual lexicon extraction from parallel corpora has already been improved via the use of an intermediary language (Kwon et al, 2013;Seo et al, 2014;Kim et al, 2015), so does statistical translation (Simard, 1999;Och and Ney, 2001). Those works lay on the assumption that another language brings additional information (Dagan and Itai, 1991).…”