Exact Decoding of Syntactic Translation Models through Lagrangian Relaxation
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We describe an exact decoding algorithm for syntax-based statistical translation. The approach uses Lagrangian relaxation to decompose the decoding problem into tractable subproblems, thereby avoiding exhaustive dynamic programming. The method recovers exact solutions, with certificates of optimality, on over 97 % of test examples; it has comparable speed to state-of-the-art decoders.