Toward machine translation with statistics and syntax and semantics

In this paper, we survey some central issues in the historical, current, and future landscape of statistical machine translation (SMT) research, taking as a starting point an extended three-dimensional MT model space. We posit a socio-geographical conceptual disparity hypothesis, that aims to explain why language pairs like Chinese-English have presented MT with so much more difficulty than others. The evolution from simple token-based to segment-based to tree-based syntactic SMT is sketched. For tree-based SMT, we consider language bias rationales for selecting the degree of compositional power within the hierarchy of expressiveness for transduction grammars (or synchronous grammars). This leads us to inversion transductions and the ITG model prevalent in current state-of-the-art SMT, along with the underlying ITG hypothesis, which posits a language universal. Against this backdrop, we enumerate a set of key open questions for syntactic SMT. We then consider the more recent area of semantic SMT. We list principles for successful application of sense disambiguation models to semantic SMT, and describe early directions in the use of semantic role labeling for semantic SMT.