Abstract
Statistical language models based on n-gram counts have been shown to successfully replace grammar rules in standard 2-stage (or ‘generate-and-select’) Natural Language Generation (NLG). In highly-inflected languages, however, the amount of training data required to cope with n-gram sparseness may be simply unobtainable, and the benefits of a statistical approach become less obvious. In this work we address the issue of text generation in a highly-inflected language by making use of factored language models (FLM) that take morphological information into account. We present a number of experiments involving the use of simple FLMs applied to various surface realisation tasks, showing that FLMs may implement 2-stage generation with results that are far superior to standard n-gram models alone.
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References
Gatt, A., Reiter, E.: SimpleNLG: A realization engine for practical applications. In: European Natural Language Generation Workshop, ENLG 2009 (2009)
Reiter, E.: An Architecture for Data-to-Text Systems. In: European Natural Language Generation Workshop (ENLG 2007), pp. 97–104 (2007)
Langkilde, I.: Forest-based statistical sentence generation. In: Proceedings of ANLP-NAACL 2000, pp. 170–177 (2000)
Belz, A.: Automatic Generation of Weather Forecast Texts using Comprehensive Probabilistic Generation-Space Models. Natural Language Engineering 14(4), 431–455 (2008)
de Novais, E.M., Dias Tadeu, T., Paraboni, I.: Improved Text Generation Using N-gram Statistics. In: Kuri-Morales, A., Simari, G.R. (eds.) IBERAMIA 2010. LNCS (LNAI), vol. 6433, pp. 316–325. Springer, Heidelberg (2010)
Nunes, M.G.V., Vieira, F.M.C., Zavaglia, C., Sossolote, C.R.C., Hernandez, J.: A construcao de um lexico para o portugues do Brasil: licoes aprendidas e perspectivas. II Encontro para o processamento de portugues escrito e Falado, 61–70 (1996)
Reiter, E., Sripada, S.: Human Variation and Lexical Choice. Computational Linguistics 28(4) (2002)
Bangalore, S., Rambow, O.: Corpus-based lexical choice in natural language generation. In: 38th Meeting of the ACL, Hong Kong, pp. 464–471 (2000)
Malouf, R.: The order of prenominal adjectives in natural language generation. In: Proceedings of ACL 2000, Hong Kong (2000)
Mitchell, M.: Class-Based Ordering of Prenominal Modifiers. In: Proceedings of the 12th European Workshop on Natural Language Generation, Athens, pp. 50–57 (2009)
Bilmes, J., Kirchhoff, K.: Factored Language Models and Generalized Parallel Backoff. In: Proceedings of HLT-NAACL 2003, vol. 2 (2003)
NIST: Automatic Evaluation of Machine Translation Quality using n-gram Co-occurrence Statistics (2002), http://www.nist.gov/speech/tests/mt/doc/ngram-study.pdf
Papineni, S., Roukos, T., Ward, W., Zhu, W.: Bleu: a method for automatic evaluation of machine translation. In: ACL 2002, pp. 311–318 (2002)
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de Novais, E.M., Paraboni, I., Ferreira, D.T. (2011). Highly-Inflected Language Generation Using Factored Language Models. In: Gelbukh, A.F. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19400-9_34
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DOI: https://doi.org/10.1007/978-3-642-19400-9_34
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