Abstract
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.
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Lang, K.J., Pearlmutter, B.A., Price, R.A.: Results of the Abbadingo One DFA learning competition and a new evidence-driven state merging algorithm. In: Honavar, V.G., Slutzki, G. (eds.) ICGI 1998. LNCS (LNAI), vol. 1433, pp. 1–12. Springer, Heidelberg (1998)
Starkie, B., Coste, F., van Zaanen, M.: The Omphalos context-free grammar learning competition. In: Paliouras, Sakakibara (eds.) [13], pp. 16–27
Starkie, B., Coste, F., van Zaanen, M.: Progressing the state-of-the-art in grammatical inference by competition. AI Communications 18(2), 93–115 (2005)
National Institute of Standards and Technology. The 2006 nist machine translation evaluation plan (mt06) (2006), http://www.nist.gov/speech/tests/mt/doc/mt06_evalplan.v4.pdf
Casacuberta, F.: Inference of finite-state transducers by using regular grammars and morphisms. In: Oliveira, A.L. (ed.) ICGI 2000. LNCS (LNAI), vol. 1891, pp. 1–14. Springer, Heidelberg (2000)
Gurari, E.: An Introduction to the Theory of Computation. Computer Science Press, Rockville (1989)
Aho, A.V., Ullman, J.D.: The theory of parsing, translation, and compiling. Prentice Hall, Englewood Cliffs (1972)
Fu, K.S.: Syntactic pattern recognition and applications. Advances in computing science and technology series. Prentice Hall, Englewood Cliffs (1982)
Valiant, L.G.: A theory of the learnable. Communications of the Association for Computing Machinery 27(11), 1134–1142 (1984)
Amengual, J.C., Castaño, A., Castellanos, A., Jiménez, V.M., Llorens, D., Marzal, A., Prat, F., Vilar, J.M., Benedi, J.M., Casacuberta, F., Pastor, M., Vidal, E.: The eutrans-i spoken language translation system. Machine Translation 15(1), 75–103 (2000)
Matusov, E., Kanthak, S., Ney, H.: Efficient statistical machine translation with constrained reordering. In: European Association for Machine Translation (EAMT) 10th Annual Conference, Budapest, Hungary, pp. 181–188 (2005)
Vidal, E., Casacuberta, F.: Learning finite-state models for machine translation. In: Paliouras, Sakakibara (eds.) [13], pp. 3–15
Paliouras, G., Sakakibara, Y. (eds.): ICGI 2004. LNCS (LNAI), vol. 3264. Springer, Heidelberg (2004)
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Starkie, B., van Zaanen, M., Estival, D. (2006). The Tenjinno Machine Translation Competition. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2006. Lecture Notes in Computer Science(), vol 4201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11872436_18
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DOI: https://doi.org/10.1007/11872436_18
Publisher Name: Springer, Berlin, Heidelberg
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