Syntax-Driven Learning of Sub-Sentential Translation Equivalents and Translation Rules from Parsed Parallel Corpora

We describe a multi-step process for automatically learning reliable sub-sentential syntactic phrases that are translation equivalents of each other and syntactic translation rules between two languages. The input to the process is a corpus of parallel sentences, word-aligned and annotated with phrase-structure parse trees. We first apply a newly developed algorithm for aligning parse-tree nodes between the two parallel trees. Next, we extract all aligned sub-sentential syntactic constituents from the parallel sentences, and create a syntax-based phrase-table. Finally, we treat the node alignments as tree decomposition points and extract from the corpus all possible synchronous parallel tree fragments. These are then converted into synchronous context-free rules. We describe the approach and analyze its application to Chinese-English parallel data.