Foundations of Text Alignment—Statistical Machine Translation Models from Bitexts to Bigrammars

This book provides a systematic, foundational introduction to automatic alignment of parallel texts, a family of essential corpus analysis techniques for computing and learning the mappings between corresponding parts of the texts. Bitext alignment lies at the heart of all data-driven machine learning approaches to automatic translation, and the rapid research progress on alignment during the past two decades underlies the success of statistical machine translation approaches. Alignment is used across a wide range of resource acquisition applications including word sense disambiguation, terminology extraction, and grammar induction, as well as in translation memories and biconcordances for translators' assistants, bilingual lexicographers, and computer assisted language learners.

Prof. Wu received his PhD in Computer Science from the University of California at Berkeley, and was a postdoctoral fellow at the University of Toronto (Ontario, Canada) prior to joining HKUST in 1992. He received his Executive MBA from Kellogg and HKUST in 2002, and a BS in Computer Engineering from the University of California at San Diego (Revelle College departmental award, cum laude, Phi Beta Kappa) in 1984. He has been a visiting researcher at Columbia University in 1995-96, Bell Laboratories in 1995, and the Technische Universitat Munchen (Munich, Germany) during 1986-87. Prof. Wu serves as Associate Editor of AI Journal and on the Editorial Board of Machine Translation and Journal of Natural Language Engineering. He has also served as Co-Chair for EMNLP-2004, and on the Editorial Board of Computational Linguistics and as Associate Editor of ACM Transactions on Speech and Language Processing, the Organizing Committee of ACL-2000 and WVLC-5 (SIGDAT 1997), and the Executive Committee of the Association for Computational Linguistics (ACL).