tailieunhanh - Báo cáo khoa học: "Fast and Scalable Decoding with Language Model Look-Ahead for Phrase-based Statistical Machine Translation"

In this work we present two extensions to the well-known dynamic programming beam search in phrase-based statistical machine translation (SMT), aiming at increased efficiency of decoding by minimizing the number of language model computations and hypothesis expansions. | Fast and Scalable Decoding with Language Model Look-Ahead for Phrase-based Statistical Machine Translation Joern Wuebker Hermann Ney Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University Germany surname@ Richard Zens Google Inc. 1600 Amphitheatre Parkway Mountain View CA 94043 zens@ Abstract In this work we present two extensions to the well-known dynamic programming beam search in phrase-based statistical machine translation SMT aiming at increased efficiency of decoding by minimizing the number of language model computations and hypothesis expansions. Our results show that language model based pre-sorting yields a small improvement in translation quality and a speedup by a factor of 2. Two look-ahead methods are shown to further increase translation speed by a factor of 2 without changing the search space and a factor of 4 with the side-effect of some additional search errors. We compare our approach with Moses and observe the same performance but a substantially better trade-off between translation quality and speed. At a speed of roughly 70 words per second Moses reaches Bleu whereas our approach yields with identical models. 1 Introduction phrase translation candidates has a positive effect on both translation quality and speed. Further we introduce two novel LM look-ahead methods. The idea of LM look-ahead is to incorporate the LM probabilities into the pruning process of the beam search as early as possible. In speech recognition it has been used for many years Steinbiss et al. 1994 Ortmanns et al. 1998 . First-word LM look-ahead exploits the search structure to use the LM costs of the first word of a new phrase as a lower bound for the full LM costs of the phrase. Phrase-only LM look-ahead makes use of a pre-computed estimate of the full LM costs for each phrase. We detail the implementation of these methods and analyze their effect with respect to the number of LM .