Abstract
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.
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Ding, Y., Palmer, M. (2005). Automatic Learning of Parallel Dependency Treelet Pairs. In: Su, KY., Tsujii, J., Lee, JH., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2004. IJCNLP 2004. Lecture Notes in Computer Science(), vol 3248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30211-7_25
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DOI: https://doi.org/10.1007/978-3-540-30211-7_25
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