Exploring the effect of semantic similarity for Phrase-based Machine Translation

Abstract

The paper investigates the use of semantic similarity scores as feature in the phrase based machine translation system. We propose the use of partial least square regression to learn the bilingual word embedding using compositional distributional semantics. The model outperforms the baseline system which is shown by an increase in BLEU score. We also show the effect of varying the vector dimension and context window for two different approaches of learning word vectors.