A joint inference of deep case analysis and zero subject generation for Japanese-to-English statistical machine translation

Taku Kudo, Hiroshi Ichikawa, Hideto Kazawa

Abstract: We present a simple joint inference of deep case analysis and zero subject generation for the pre-ordering in Japanese-toEnglish machine translation. The detection of subjects and objects from Japanese sentences is more difficult than that from English, while it is the key process to generate correct English word orders. In addition, subjects are often omitted in Japanese when they are inferable from the context. We propose a new Japanese deep syntactic parser that consists of pointwise probabilistic models and a global inference with linguistic constraints. We applied our new deep parser to pre-ordering in Japanese-toEnglish SMT system and show substantial improvements in automatic evaluations.