Mining Bilingual Word Pairs from Comparable Corpus using Apache Spark Framework

Bilingual dictionaries are essential resources in many areas of natural language processing tasks, but resource-scarce and less popular language pairs rarely have such. Efficient automatic methods for inducting bilingual dictionaries are needed as manual resources and efforts are scarce for low-resourced languages. In this paper, we induce word translations using bilingual embedding. We use the Apache Spark framework for parallel computation. Further, to validate the quality of the generated bilingual dictionary, we use it in a phrase-table aided Neural Machine Translation (NMT) system. The system can perform moderately well with a manual bilingual dictionary; we change this into our inducted dictionary. The corresponding translated outputs are compared using the Bilingual Evaluation Understudy (BLEU) and Rank-based Intuitive Bilingual Evaluation Score (RIBES) metrics.