Syntax based reordering has been shown to be an effective way of handling word order differences between source and target languages in Statistical Machine Translation (SMT) systems. We present a simple, automatic method to learn rules that reorder source sentences to more closely match the target language word order using only a source side parse tree and automatically generated alignments. The resulting rules are applied to source language inputs as a pre-processing step and demonstrate significant improvements in SMT systems across a variety of languages pairs including English to Hindi, EnglishtoSpanishandEnglishtoFrench as measured on a variety of internal test sets as well as a public test set. 1

Preordering of source side sentences has proved to be useful in improving statistical machine translation. Most work has used a parser in the source language along with rules to map the source language word order into the target language word order. The requirement to have a source language parser is a major drawback, which we seek to overcome in this paper. Instead of using a parser and then using rules to order the source side sentence we learn a model that can directly reorder source side sentences to match target word order using a small parallel corpus with highquality word alignments. Our model learns pairwise costs of a word immediately preceding another word. We use the Lin-Kernighan heuristic to find the best source reordering efficiently during training and testing and show that it suffices to provide good quality reordering. We show gains in translation performance based on our reordering model for translating from Hindi to English, Urdu to English (with a public dataset), and English to Hindi. For English to Hindi we show that our technique achieves better performance than a method that uses rules applied to the source side English parse. 1

Machine Translation (MT) is a task with multiple components, each of which can be very challenging. This thesis focuses on a difficult language pair – Chinese to English – and works on several language-specific aspects that make translation more difficult. The first challenge this thesis focuses on is the differences in the writing systems. In Chinese there are no explicit boundaries between words, and even the definition of a “word” is unclear. We build a general purpose Chinese word segmenter with linguistically inspired features that performs very well on the SIGHAN 2005 bakeoff data. Then we study how Chinese word segmenter performance is related to MT performance, and provide a way to tune the “word ” unit in Chinese so that it can better match up with the English word granularity, and therefore improve MT performance. The second challenge we address is different word order between Chinese and English. We first perform error analysis on three state-of-the-art MT systems to see what the most prominent problems are, especially how different word orders cause translation errors. According to our findings, we propose two solutions to improve Chinese-to-English