Enriching low resource statistical machine translation using induced bilingual lexicons

Bel Rafecas, Núria; Jingyi, Han

Enriching low resource statistical machine translation using induced bilingual lexicons

Citation

Jingyi H, Bel N. Enriching low resource statistical machine translation using induced bilingual lexicons. Procesamiento del Lenguaje Natural. 2017;59: 91-8.

Abstract

In this work we present an experiment for enriching a Statistical Machine Translation (SMT) phrase table with automatically created bilingual word pairs. The bilingual lexicon is induced with a supervised classifier trained using a joint representation of word embeddings (WE) and Brown clusters (BC) of translation equivalent word pairs as features. The classifier reaches a 0.94 F-score and the MT experiment results show an improvement of up to +0.70 BLEU over a low resource Chinese-Spanish phrase-based SMT baseline, demonstrating that bad entries delivered by the classi er are well handled.