Improvements in hierarchical phrase-based Statistical Machine Translation

TL;DRAbstract

Hierarchical phrase-based translation (Hiero) is a statistical machine translation (SMT) model that encodes translation as a synchronous context-free grammar derivation between source and target language strings (Chiang, 2005; Chiang, 2007). Hiero models are more powerful than phrase-based models in capturing complex source-target reordering as well as discontiguous phrases, while being easier to estimate and decode with compared to their full syntax-based counterparts. In this thesis, we propose improvements to two broad aspects of the Hiero translation pipeline: i) learning Hiero translation model and estimating their parameters and ii) parameter tuning for discriminative log-linear models that are used to decode with such features. We use our own open-source implementation of Hiero called Kriya (Sankaran et al., 2012b) for all the experiments in this thesis. This thesis contains the following specific contributions: We propose a Bayesian model for learning Hiero grammars as an alter

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