Automatic Learning of Parallel Dependency Treelet Pairs

摘要

Induction of synchronous grammars from empirical data has long been a unsolved problem; despite generative synchronous grammar theoretically suits the machine translation task very well. This fact is mainly due to pervasive structural divergences between languages. This paper presents a statistical approach that learns dependency structure mappings from parallel corpora. The algorithm introduced in this paper extends the dependency tree word alignment algorithm in (Ding et al., 2003). The new algorithm automatically learns parallel dependency treelet pairs from loosely matched non-isomorphic dependency trees while keeping computational complexity polynomial in the length of the sentences. A set of heuristics is introduced and specifically optimized for parallel treelet learning purposes using Minimum Error Rate training. As learning parallel syntactic structures is the key step in the automatic learning of a synchronous .grammar, the learnt parallel dependency treelet pairs in our approach serve as an important first step of any lexicalized synchronous grammar induction.