Speech Translation with Grammar Driven Probabilistic Phrasal Bilexica Extraction

We introduce a new type of transduction grammar that allows for learning of probabilistic phrasal bilexica, leading to a significant improvement in spoken language translation accuracy. The current state-of-the-art in statistical machine translation relies on a complicated and crude pipeline to learn probabilistic phrasal bilexica—the very core of any speech translation system. In this paper, we present a more principled approach to learning probabilistic phrasal bilexica, based on stochastic transduction grammar learning applicable to speech corpora.