Induction of synchronous grammars from empirical data has long been an unsolved problem; despite generative synchronous grammars theoretically suit 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 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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Ding, Y., & Palmer, M. (2005). Automatic learning of parallel dependency treelet pairs. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3248, pp. 233–243). Springer Verlag. https://doi.org/10.1007/978-3-540-30211-7_25
Mendeley helps you to discover research relevant for your work.