Learning Hierarchical Translation Structure with Linguistic Annotations

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 642–652, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Learning Hierarchical Translation Structure with Linguistic Annotations Markos Mylonakis ILLC University of Amsterdam m.mylonakis@uva.nl Khalil Sima’an ILLC University of Amsterdam k.simaan@uva.nl Abstract While it is generally accepted that many trans- lation phenomena are correlated with linguis- tic structures, employing linguistic syntax for translation has proven a highly non-trivial task. The key assumption behind many ap- proaches is that translation is guided by the source and/or target language parse, employ- ing rules extracted from the parse tree or performing tree transformations. These ap- proaches enforce strict constraints and might overlook important translation phenomena that cross linguistic constituents. We propose a novel flexible modelling approach to intro- duce linguistic information of varying gran- ularity from the source side. Our method induces joint probability synchronous gram- mars and estimates their parameters, by select- ing and weighing together linguistically moti- vated rules according to an objective function directly targeting generalisation over future data. We obtain statistically significant im- provements across 4 different language pairs with English as source, mounting up to +1.92 BLEU for Chinese as target. 1 Introduction Recent advances in Statistical Machine Translation (SMT) are widely centred around two concepts: (a) hierarchical translation processes, frequently employing Synchronous Context Free Grammars (SCFGs) and (b) transduction or synchronous rewrite processes over a linguistic syntactic tree. SCFGs in the form of the Inversion-Transduction Grammar (ITG) were first introduced by (Wu, 1997) as a formalism to recursively describe the trans- lation process. The Hiero system (Chiang, 2005) utilised an ITG-flavour which focused on hierarchi- cal phrase-pairs to capture context-driven translation and reordering patterns with ‘gaps’, offering com- petitive performance particularly for language pairs with extensive reordering. As Hiero uses a single non-terminal and concentrates on overcoming trans- lation lexicon sparsity, it barely explores the recur- sive nature of translation past the lexical level. Nev- ertheless, the successful employment of SCFGs for phrase-based SMT brought translation models as- suming latent syntactic structure to the spotlight. Simultaneously, mounting efforts have been di- rected towards SMT models employing linguistic syntax on the source side (Yamada and Knight, 2001; Quirk et al., 2005; Liu et al., 2006), target side (Galley et al., 2004; Galley et al., 2006) or both (Zhang et al., 2008; Liu et al., 2009; Chiang, 2010). Hierarchical translation was combined with target side linguistic annotation in (Zollmann and Venu- gopal, 2006). Interestingly, early on (Koehn et al., 2003) exemplified the difficulties of integrating lin- guistic information in translation systems. Syntax- based MT often suffers from inadequate constraints in the translation rules extracted, or from striving to combine these rules together towards a full deriva- tion. Recent research tries to address these issues, by re-structuring training data parse trees to bet- ter suit syntax-based SMT training (Wang et al., 2010), or by moving from linguistically motivated synchronous grammars to systems where linguistic plausibility of the translation is assessed through ad- ditional features in a …

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