A Hierarchical Phrase-Based Model for Statistical Machine Translation

Proceedings of the 43rd Annual Meeting of the ACL, pages 263–270, Ann Arbor, June 2005. c⃝2005 Association for Computational Linguistics A Hierarchical Phrase-Based Model for Statistical Machine Translation David Chiang Institute for Advanced Computer Studies (UMIACS) University of Maryland, College Park, MD 20742, USA dchiang@umiacs.umd.edu Abstract We present a statistical phrase-based transla- tion model that uses hierarchical phrases— phrases that contain subphrases. The model is formally a synchronous context-free gram- mar but is learned from a bitext without any syntactic information. Thus it can be seen as a shift to the formal machinery of syntax- based translation systems without any lin- guistic commitment. In our experiments us- ing BLEU as a metric, the hierarchical phrase- based model achieves a relative improve- ment of 7.5% over Pharaoh, a state-of-the-art phrase-based system. 1 Introduction The alignment template translation model (Och and Ney, 2004) and related phrase-based models ad- vanced the previous state of the art by moving from words to phrases as the basic unit of transla- tion. Phrases, which can be any substring and not necessarily phrases in any syntactic theory, allow these models to learn local reorderings, translation of short idioms, or insertions and deletions that are sensitive to local context. They are thus a simple and powerful mechanism for machine translation. The basic phrase-based model is an instance of the noisy-channel approach (Brown et al., 1993),1 in which the translation of a French sentence f into an 1Throughout this paper, we follow the convention of Brown et al. of designating the source and target languages as “French” and “English,” respectively. The variables f and e stand for source and target sentences; f j i stands for the substring of f from position i to position j inclusive, and similarly for e j i . English sentence e is modeled as: arg max e P(e | f) = arg max e P(e, f) (1) = arg max e (P(e) × P( f | e)) (2) The translation model P(f | e) “encodes” e into f by the following steps: 1. segment e into phrases ¯e1 · · · ¯eI, typically with a uniform distribution over segmentations; 2. reorder the ¯ei according to some distortion model; 3. translate each of the ¯ei into French phrases ac- cording to a model P( ¯f | ¯e) estimated from the training data. Other phrase-based models model the joint distribu- tion P(e, f) (Marcu and Wong, 2002) or made P(e) and P(f | e) into features of a log-linear model (Och and Ney, 2002). But the basic architecture of phrase segmentation (or generation), phrase reordering, and phrase translation remains the same. Phrase-based models can robustly perform trans- lations that are localized to substrings that are com- mon enough to have been observed in training. But Koehn et al. (2003) find that phrases longer than three words improve performance little, suggesting that data sparseness takes over for longer phrases. Above the phrase level, these models typically have a simple distortion model that reorders phrases in- dependently …

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