New Statistical and Syntactic Models for Machine Translation

Significant improvements have been achieved in machine translation (MT) over the past few years, mostly motivated by the appearance of statistical machine translation (SMT) technology, which is currently considered the best way to perform MT of natural languages. The main goal of this thesis is to enhance the classical SMT models, introducing syntactical knowledge in the pre-translation step by reordering the source side of the corpus. To a great extent, our interest is in the value of syntax in reordering for languages with high word order disparity. A secondary objective consists of determining the potential of different language model (LM) enhancement techniques in order to improve the performance and efficiency of SMT systems. We start with a comprehensive study of the SMT state-of-the-art, describing the fundamental models underlying the translation process, along with a brief description of the main methods of automatic evaluation of translation quality. We emphasize phrase-based and N -gram-based SMT, analyzing the major differences between these two approaches. Subsequently, we concentrate on language modeling methods that have not received much attention in the SMT community. We report on experiments in applying N -grambased SMT system adaptation to a speech transcription task, describe a positive impact of accurate cut-off threshold selection both on the model size and LM noisiness, and finally present a continuous-space LM, estimated in the form of an artificial neural network. Moreover, we propose a novel syntax-based approach to handle the fundamental problem of word ordering for SMT exploiting syntactic representations of source and target texts. The idea of augmenting SMT by using a syntax-based reordering step prior to translation, proposed in recent years, has been quite successful in improving translation quality, especially for translation between languages with high word order disparity. We provide the reader with a thorough study of the state-of-the-art reordering techniques and introduce a new classification of reordering algorithms for SMT. We then propose a