| Machine Translation (MT) is the task of translating a document from a source language (e.g., Chinese) into a target language (e.g., English) via computer. State-of-the-art statistical approaches to MT use large collections of human-translated documents as training material, gathering statistics on the patterns of correspondence between languages according to the features specified by the translation model. Using this bilingual translation model in conjunction with a target language model, created by gathering statistics from a large monolingual corpus, a new document in the source language can be automatically translated into its target-language equivalent with surprising accuracy.;Much MT research focuses on types of the patterns and features to include in a translation model. Recent statistical MT models have used syntax trees to enforce grammaticality, but the currently popular tree substitution models only memorize sequences of words or constituents, specifying exactly what phrases to use and exactly what trees are grammatical, which does not generalize well. Adding the operation of tree-adjoining provides the freedom to splice additional information into an existing grammatical tree. An adjoining translation model allows general, linguistically-motivated translation patterns to be learned without the clutter of endless variations of optional material. The appropriate modifiers, such as adjectives, adverbs, and prepositional phrases, can be grafted into these core patterns as needed to translate details. We show that the increased generalization power provided by adjoining, when used carefully, improves MT quality without becoming computationally intractable.;In this thesis, we describe challenges encountered by both word-sequence-based and syntax-tree-based MT systems today, and present an in-depth, quantitative comparison of both models. Then we describe a novel model for statistical MT which addresses these challenges using a synchronous tree-adjoining grammar. We introduce a method of converting these grammars to a weakly equivalent tree transducer for decoding. Then we present a method for learning the rules and associated probabilities of this grammar from aligned tree/string training data, and empirically analyze important characteristics of the resulting model, considering and evaluating many variations. Finally, our results show that adjoining delivers a consistent improvement over a baseline statistical syntax-based MT model on both medium and large-scale MT tasks using several language pairs. |
