Dennis N. Mehay and Chris Brew (2012) CCG Syntactic Reordering Models for Phrase-based Machine Translation. In Proceedings of WMT-2012, Montreal, Canada.
2007
Mehay, Dennis and Chris Brew (2007) BLEUATRE: Flattening Syntactic Dependencies for MT Evaluation The 11th International Conference on Theoretical and Methodological Issues in Machine Translation (TMI-07)
2002
Davis, Paul C. and Chris Brew. (2002). Stone Soup Translation. In Proceedings of the 9th Conference on Theoretical and Methodological Issues in Machine Translation (TMI-2002), pages 31–41, Keihanna, Japan.
1994
Brew, Chris and Henry S. Thompson. (1994). Automatic evaluation of computer generated text: a progress report on the Texteval project. In Clifford Weinstein, editor, Proceedings of the Workshop on Human Language Technology, pages 108–113. ARPA/ISTO, March.
1992
Brew, Chris (1992). Letting the Cat out of the Bag: generation for shake-and-bake MT. In Proceedings of the 14th International Conference on Computational Linguistics, pages 610–616, Nantes, France.
Ph.D theses
Dennis Mehay (tbd.)
Bean Soup Translation: Flexible, Linguistically-motivated Syntax for Machine Translation
The main contribution of this thesis is to use the flexible syntax of Combinatory Categorial Grammar [CCG, Steedman, 2000] as the basis for deriving syntactic constituent labels for target strings in phrase-based systems, providing CCG labels for many target strings that traditional syntactic theories struggle to describe. These CCG labels are used to train novel syntax-based reordering and language models, which efficiently describe translation reordering patterns, as well as assess the grammaticality of target translations. The models are easily incorporated into phrase-based systems with minimal disruption to existing technology and achieve superior automatic metric scores and human evaluation ratings over a strong phrase-based baseline, as well as over syntax-based techniques that do not use CCG.
Paul C. Davis Motorola Research
Stone Soup Translation: The Linked Automata Model
This dissertation introduces and begins an investigation of an MT model consisting of a novel combination finite-state devices. The model proposed is more flexible than transducer models, giving increased ability to handle word order differences between languages, as well as crossing and discontinuous alignments between words. The linked automata MT model consists of a source language automaton, a target language automaton, and an alignment table—a function which probabilistically links sequences of source and target language transitions. It is this augmentation to the finite-state base which gives the linked automata model its flexibility.
The dissertation describes the linked automata model from the ground up, beginning with a description of some of the relevant MT history and empirical MT literature, and the preparatory steps for building the model, including a detailed discussion of word alignment and the introduction of a new technique for word alignment evaluation. Discussion then centers on the description of the model and its use of probabilities, including algorithms for its construction from word-aligned bitexts and for the translation process. The focus next moves to expanding the linked automata approach, first through generalization and techniques for extracting partial results, and then by increasing the coverage, both in terms of using additional linguistic information and using more complex alignments. The dissertation presents preliminary results for a test corpus of English to Spanish translations, and suggests ways in which the model can be further expanded as the foundation of a more powerful MT system.