We present the Carnegie Mellon University Stat-XFER group submission to the WMT 2010 shared translation task. Updates to our syntax-based SMT system mainly fell in the areas of new feature formulations in the translation model and improved filtering of SCFG rules. Compared to our WMT 2009 submission, we report a gain of 1.73 BLEU by using the new features and decoding environment, and a gain of up to 0.52 BLEU from improved grammar selection. 1

In this paper we apply lightly-supervised training to a hierarchical phrase-based statistical machine translation system. We employ bitexts that have been built by automatically translating large amounts of monolingual data as additional parallel training corpora. We explore different ways of using this additional data to improve our system. Our results show that integrating a second translation model with only non-hierarchical phrases extracted from the automatically generated bitexts is a reasonable approach. The translation performance matches the result we achieve with a joint extraction on all training bitexts while the system is kept smaller due to a considerably lower overall number of phrases. 1

This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. It has not been submitted in whole or in part for a degree at any other university. Some of the work has been published previously in conference proceedings (Blackwood et al., 2008a; Blackwood

We report on investigations into hierarchical phrase-based translation grammars based on rules extracted from posterior distributions over alignments of the parallel text. Rather than restrict rule extraction to a single alignment, such as Viterbi, we instead extract rules based on posterior distributions provided by the HMM word-to-word alignment model. We define translation grammars progressively by adding classes of rules to a basic phrase-based system. We assess these grammars in terms of their expressive power, measured by their ability to align the parallel text from which their rules are extracted, and the quality of the translations they yield. In Chinese-to-English translation, we find that rule extraction from posteriors gives translation improvements. We also find that grammars with rules with only one nonterminal, when extracted from posteriors, can outperform more complex grammars extracted from Viterbi alignments. Finally, we show that the best way to exploit source-totarget and target-to-source alignment models is to build two separate systems and combine their output translation lattices. 1

This degree is submitted to the University of Cambridge

This paper describes Kriya — a new statistical machine translation (SMT) system that uses hierarchical phrases, which were first introduced in the Hiero machine translation system (Chiang, 2007). Kriya supports both a grammar extraction module for synchronous context-free grammars (SCFGs) and a CKY-based decoder. There are several re-implementations of Hiero in the machine translation community, but Kriya offers the following novel contributions: (a) Grammar extraction in Kriya supports extraction of the full set of Hiero-style SCFG rules but also supports the extraction of several types of compact rule sets which leads to faster decoding for different language pairs without compromising the BLEU scores. Kriya currently supports extraction of compact SCFGs such as grammars with one non-terminal and grammar pruning based on certain rule patterns, and (b) The Kriya decoder offers some unique improvements in the implementation of cube pruning, such as increasing diversity in the target language n-best output and novel methods for language model (LM) integration. The Kriya decoder can take advantage of parallelization using a networked cluster. Kriya supports KENLM and SRILM for language model queries and exploits n-gram history states in KENLM. This paper also provides several experimental results which demonstrate that the translation quality of Kriya compares favourably to the Moses (Koehn et al., 2007) phrase-based system in several language pairs while showing a substantial improvement for Chinese-English similar to Chiang (2007). We also quantify the model sizes for phrase-based and Hiero-style systems apart from presenting experiments comparing variants of Hiero models. 1.