Abstract
The interest in using Finite-State Models in a large variety of applications is recently growing as more powerful techniques for learning them from examples have been developed. Language Understanding can be approached this way as a problem of language translation in which the target language is a formal language rather than a natural one. Finite-state transducers are used to model the translation process, and are automatically learned from training data consisting of pairs of natural-language/formal-language sentences. The need for training data is dramatically reduced by performing a two-level learning process based on lexical/phrase categorization. Successful experiments are presented on a task consisting in the “understanding” of Spanish natural-language sentences describing dates and times, where the target formal language is the one used in the popular Unix command “at”.
Work partially supported by EuTrans, European Union ESPRIT LTR Project 30268, and the Spanish CICYT under grant TIC-0745-CO2.
Author supported by a FPI grant from the Conselleria d'Educació i Ciència of Valencian Government.
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J.C Amengual, J.B. Benedí, F. Casacuberta, A. Castaño, A. Castellanos, D. Llorens, A. Marzal, F. Prat, E. Vidal and J.M. Vilar: “Using Categories in the Eutrans System”. ACL-ELSNET Workshop on Spoken Language Translation, Madrid, Spain, pp. 44–52. (1997)
J.C. Amengual, E. Vidal. Two Different Approaches for Cost-efficient Viterbi Parsing with Error Correction. Proc. of the SSPR'96, IAPR International Workshop on Structural and Syntactical Pattern Recognition, August 20–23, 1996, Leipzig. To be published in the Proceedings.
J.C. Amengual, E. Vidal and J.M. Benedí. “Simplifying Language through Error-Correcting Decoding”. Proceedings of the ICSLP96 (IV International Conference on Spoken Language Processing). To be published. October, 1996.
J. Berstel. Transductions and Context-Free Languages. Teubner, Stuttgart. 1979.
P.F. Brown et al.. “A Statistical Approach to Machine Translation”. Computational Linguistics, Vol. 16, No.2, pp.79–85, 1990.
A. Castellanos, E. Vidal, J. Oncina. “Language Understanding and Subsequential Transducer Learning”. 1st International Colloquium on Grammatical Inference,Colchester, England. proc., pp. 11/1–11/10. April, 1993.
J.G. Bauer, H. Stahl, J. Mller: “A One-pass Search Algorithm for Understanding Natural Spoken Time Utterances by Stochastic Models”. Proc. of the EUROSPEECH'95, Madrid, Spain, vol.I, pp. 567–570. (1995)
F. Casacuberta: “Maximum Mutual Information and Conditional Maximum Likelihood Estimations on Stochastic Regular Syntax-Directed TranslationSchemes”, Lecture notes in Artificial Intelligence, vol.1147, pp. 282–291, Springer-Verlag. (1996)
F. Casacuberta: “Growth Transformations for Probabilistic Functions on Stochastic Grammars”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 10, n. 3, pp. 183–201, Word Scientific Publishing Company. (1996)
C.T. Hemphill, J.J. Godfrey, G.R. Doddington. “The ATIS Spoken Language Systems, pilot Corpus”. Proc. of 3rd DARPA Workshop on Speech and Natural Language, pp. 102–108, Hidden Valley (PA), June 1990.
F. Jelinek: “Language Modeling for Speech Recognition”. In [13] (1996).
V. Jimenez, A. Castellanos, E. Vidal. “Some results with a trainable speech translation and understanding system”. In Proceedings of the ICASSP-95, Detroit, MI (USA), 1995
A. Kornai (ed.); Proceedings of the ECAI'96 Workshop: Extended Finite State Models of Language. Budapest, 1996.
A. Lavie, A. Waibel, L. Levin, M. Finke, D. Gates, M. Gavaldà, T. Zeppenfeld and P. Zhan: “JANUS-III: Speech-to-speech Translation in Multiple Languages”, Proc. of the ICASSP'97, Munich, Germany, vol.I, pp. 99–102. (1997)
E. Maier and S. McGlashan: “Semantic and Dialogue Processing in the VERBMOBIL Spoken Dialogue Translation System”, In Proceedings in Artificial Intelli-gence: CRIM/FORWISS Workshop on Progress and Prospects of Speech Research and Technology, H. Niemann, R. de Mori and G. Hanrieder (eds.), Infix, pp. 270–273. (1994)
J. Oncina. “Aprendizaje de Lenguages RegulÄres y Funciones Subsecuenciales”. Ph.D. diss., Universidad Politecnica de Valencia, 1991.
J. Oncina, P. Garcia, E. Vidal. “Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.15, No.5, pp.448–458. May, 1993.
J. Oncina, A. Castellanos, E. Vidal, V. Jimenez. “Corpus-Based Machine Translation through Subsequential Transducers”. Third Int. Conf. on the Cognitive Science of Natural Language Processing, proc., Dublin, 1994
J. Oncina, M.A. Var. “Using domain information during the learning of a subsequential transducer”. In Laurent Miclet and Colin de la Higuera, editors, Gram-matical Inference: Learning Syntax from Sentences, Lecture Notes in Computer Science, vol. 1147, pp. 301–312. Springer-Verlag. 1996
R. Pieraccini, E. Levin. “Stochastic Representation of Semantic Structure for Speech Understanding”. EUROSPEECH'91, Proc., Vol. 2, pp.383–386. Genoa Sept, 1991.
R. Pieraccini, E. Levin, E. Vidal. “Learning How To Understand Language”. EUROSPEECH'93, proc., Vol.2, pp. 1407–1412. Berlin, Sept, 1993.
N. Prieto, E. Vidal. “Learning Language Models through the ECGI method”. Speech Communication, No.11, pp.299–309. 1992.
K. Seymore, R. Rosenfeld. “Scalable Backoff Language Models”. ICSLP-96, proc. pp.232–235. Philadelfia, 1996.
E. Vidal: “Language Learning, Understanding and Translation”, In Proc. in Art. Intell.: CRIM/FORWISS Workshop on Progress and Prospects of Speech Research and Technology, H. Niemann, R. de Mori and G. Hanrieder (eds.), pp. 131–140. Infix, (1994).
E. Vidal: “Finite-State Speech-to-speech Translation”, Proc. of the ICASSP'97,Munich, Germany, vol.1, pp. 111–122. (1997)
E. Vidal, F. Casacuberta, P. Garcia. “Grammatical Inference and Automatic Speech Recognition”. In Speech Recognition and Coding. New Advances and Trends, J.Rubio and J.M.Lopez, Eds. Springer Verlag, 1994.
E. Vidal, D. Llorens. “Using knowledge to improve N-Gram Language Modeling through the MGGI methodology”. In Grammatical Inference: Learning Syntax from Sentences, L.Miclet, C.De La Higuera, Eds. LNAI (1147), Springer-Verlag, 1996.
J.M. Vilar, A. Marzal, E. Vidal: “Learning Language Translation in Limited Domains using Finite-State Models: some Extensions and Improvements”. Proceedings of the EUROSPEECH-95, Madrid, Spain, pp. 1231–1234. (1995)
J.M. Vilar, E. Vidal and J.C. Amengual: “Learning Extended Finite State Models for Language Translation”. Proceedings of the ECAI96 (12th European Conference on Artificial Intelligence). August (1996).
Linux system documentacion, at directory “/usr/doc/at” (Debian distribution). Also, see “man at” on a Unix system.
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Picó, D., Vidal, E. (1998). Transducer-learning experiments on language understanding. In: Honavar, V., Slutzki, G. (eds) Grammatical Inference. ICGI 1998. Lecture Notes in Computer Science, vol 1433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054071
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DOI: https://doi.org/10.1007/BFb0054071
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