Left-to-Right Hierarchical Phrase-based Machine Translation

Hierarchical phrase-based translation (Hiero for short) models statistical machine translation (SMT) using a lexicalized synchronous context-free grammar (SCFG) extracted from word aligned bitexts. The standard decoding algorithm for Hiero uses a CKY-style dynamic programming algorithm with time complexity O(n3) for source input with n words. Scoring target language strings using a language model in CKY-style decoding requires two histories per hypothesis making it significantly slower than phrase-based translation which only keeps one history per hypothesis. In addition, the size of a Hiero SCFG grammar is typically much larger than phrase-based models when extracted from the same data which also slows down decoding. In this thesis we address these issues in Hiero by adopting a new translation model and decoding algorithm called Left-to-Right hierarchical phrase-based translation (LR-Hiero for short). LR-Hiero uses a constrained form of lexicalized SCFG rules to encode translation, where the target-side is constrained to be prefix-lexicalized. LRHiero uses a decoding algorithm with time complexity O(n2) that generates the target language output in left-to-right manner which keeps only one history per hypothesis resulting in faster decoding for Hiero grammars. The thesis contains the following contributions: • We propose a novel dynamic programming algorithm for the rule extraction phase. Unlike traditional Hiero rule extraction which performs a brute-force search, LR-Hiero rule extraction is linear in the number of rules. • We propose an augmented version of LR-decoding algorithm previously proposed by Watanabe et al. [90]. Our modified LR-decoding algorithm addresses issues related to decoding time and translation quality and is shown to be more efficient than the CKY decoding algorithm in our experimental results. • We extend our LR-decoding algorithm to capture all hierarchical phrasal alignments that are reachable in CKY-style decoding algorithms. • We introduce a lexicalized reordering model to LR-Hiero that significantly improves the translation quality.