A Systematic Comparison of Phrase-Based, Hierarchical and Syntax-Augmented Statistical MT

Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 1145–1152 Manchester, August 2008 A Systematic Comparison of Phrase-Based, Hierarchical and Syntax-Augmented Statistical MT Andreas Zollmann∗and Ashish Venugopal∗and Franz Och and Jay Ponte Google Inc. 1600 Amphitheatre Parkway Mountain View, CA 94303, USA {zollmann,ashishv}@cs.cmu.edu {och,ponte}@google.com Abstract Probabilistic synchronous context-free grammar (PSCFG) translation models define weighted transduction rules that represent translation and reordering oper- ations via nonterminal symbols. In this work, we investigate the source of the im- provements in translation quality reported when using two PSCFG translation mod- els (hierarchical and syntax-augmented), when extending a state-of-the-art phrase- based baseline that serves as the lexical support for both PSCFG models. We isolate the impact on translation quality for several important design decisions in each model. We perform this comparison on three NIST language translation tasks; Chinese-to-English, Arabic-to-English and Urdu-to-English, each representing unique challenges. 1 Introduction Probabilistic synchronous context-free grammar (PSCFG) models define weighted transduction rules that are automatically learned from parallel training data. As in monolingual parsing, such rules make use of nonterminal categories to gener- alize beyond the lexical level. In the example be- low, the French (source language) words “ne” and “pas” are translated into the English (target lan- guage) word “not”, performing reordering in the context of a nonterminal of type “VB” (verb). VP → ne VB pas, do not VB : w1 ∗Work done during internships at Google Inc. ∗c⃝ 2008. Licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported li- cense (http://creativecommons.org/licenses/by-nc-sa/3.0/). Some rights reserved. VB → veux, want : w2 . As with probabilistic context-free grammars, each rule has a left-hand-side nonterminal (VP and VB in the two rules above), which constrains the rule’s usage in further composition, and is assigned a weight w, estimating the quality of the rule based on some underlying statistical model. Transla- tion with a PSCFG is thus a process of compos- ing such rules to parse the source language while synchronously generating target language output. PSCFG approaches such as Chiang (2005) and Zollmann and Venugopal (2006) typically begin with a phrase-based model as the foundation for the PSCFG rules described above. Starting with bilingual phrase pairs extracted from automatically aligned parallel text (Och and Ney, 2004; Koehn et al., 2003), these PSCFG approaches augment each contiguous (in source and target words) phrase pair with a left-hand-side symbol (like the VP in the example above), and perform a generalization procedure to form rules that include nonterminal symbols. We can thus view PSCFG methods as an attempt to generalize beyond the purely lexi- cal knowledge represented in phrase based mod- els, allowing reordering decisions to be explicitly encoded in each rule. It is important to note that while phrase-based models cannot explicitly repre- sent context sensitive reordering effects like those in the example above, in practice, phrase based models often have the potential to generate the same target translation output by translating source phrases out of order, and allowing empty trans- lations for some source words. Apart from one …

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