Putting Text-Level Linguistics into Statistical Machine Translation

Since its inception in the 1950s, and especially in the past 20 years, machine translation has made less and less use of linguistics • State-of-the-art MT is slipping down the MT pyramid • From rule-based, to example-based, to statistical systems – Within rule-based: from inter-lingua (representing meaning), to transfer (syntactic), to direct 2 The success of statistical MT • “Whenever I fire a linguist, our system performance improves” – said Frederik Jelinek around 1980, marking the statistical turn in automatic speech recognition, followed later by machine translation • What is statistical MT? – translation as a noisy channel (Weaver 1947, then Brown et al. 1993) 1. Learn n-gram based translation and language models. 2. Decode the source sentence: find the target sentence that maximizes the probabilities given by the translation model and the language model. • State of the art performance – phrase-based or hierarchical SMT, or direct rule-based M