Communications of the ACM
An Introduction to the Theory of Computation
An Introduction to the Theory of Computation
The theory of parsing, translation, and compiling
The theory of parsing, translation, and compiling
The EuTrans Spoken Language Translation System
Machine Translation
ICGI '98 Proceedings of the 4th International Colloquium on Grammatical Inference
Inference of Finite-State Transducers by Using Regular Grammars and Morphisms
ICGI '00 Proceedings of the 5th International Colloquium on Grammatical Inference: Algorithms and Applications
Towards Machine Learning of Grammars and Compilers of Programming Languages
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
A framework for the competitive evaluation of model inference techniques
Proceedings of the First International Workshop on Model Inference In Testing
Distributional learning of some context-free languages with a minimally adequate teacher
ICGI'10 Proceedings of the 10th international colloquium conference on Grammatical inference: theoretical results and applications
Using Contextual Representations to Efficiently Learn Context-Free Languages
The Journal of Machine Learning Research
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This paper describes the Tenjinno Machine Translation Competition held as part of the International Colloquium on Grammatical Inference 2006. The competition aimed to promote the development of new and better practical grammatical inference algorithms used in machine translation. Tenjinno focuses on formal models used in machine translation. We discuss design issues and decisions made when creating the competition. For the purpose of setting the competition tasks, a measure of the complexity of learning a transducer was developed. This measure has enabled us to compare the competition tasks to other published results, and it can be seen that the problems solved in the competition were of a greater complexity and were solved with lower word error rates than other published results. In addition the complexity measures and benchmark problems can be used to track the progress of the state-of-the-art into the future.