Controlled Ascent: Imbuing Statistical MT with Linguistic Knowledge

Abstract

We explore the intersection of rule-based and statistical approaches in machine translation, with a particular focus on past and current work at Microsoft Research. Until about 10 years ago, the only machine translation systems worth using were rule-based and linguistically-informed. Along came statistical approaches, which use large corpora to directly guide translations toward expressions people would actually say. Rather than making local decisions when writing and conditioning rules, goodness of translation was modeled numerically and free parameters were selected to optimize that goodness. This led to huge improvements in translation quality as more and more data was consumed. By necessity, the pendulum is swinging back towards the inclusion of linguistic features in MT systems. We describe some of our statistical and non-statistical attempts to incorporate linguistic insights into machine translation systems, showing what is currently working well, and what isn’t. We also look at trade-offs in using linguistic knowledge (“rules”) in pre- or post-processing by language pair, with a particular eye on the return on investment as training data increases in size.