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Extractive summarization using siamese hierarchical transformer encoders
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Desarrollo de algoritmos para el aprendizaje automático de palabras en idiomas desconocidos a partir de audio.
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Summarization of Spanish Talk Shows with Siamese Hierarchical Attention Networks
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Siamese hierarchical attention networks for extractive summarization
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ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation
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Detección automática de segmentos acústicos e inferencia de unidades lingüísticas.
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Traducción Automática usando conocimiento semántico en un dominio restringido ; Automatic translation using semantic knowledge in a restricted domain
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Traducción Automática usando conocimiento semántico en un dominio restringido ; Automatic translation using semantic knowledge in a restricted domain
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A Train-on-Target Strategy for Multilingual Spoken Language Understanding
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A Strategy for Multilingual Spoken Language Understanding Based on Graphs of Linguistic Units
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Multilingual Spoken Language Understanding using graphs and multiple translations
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Exploiting multiple ASR outputs for a spoken language understanding task
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ELIRF at MEDIAEVAL 2013: Similar Segments of Social Speech Task
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A phonetic-based approach to query-by-example spoken term detection
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Using word graphs as intermediate representation of uttered sentences
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A multilingual SLU system based on semantic decoding of graphs of words
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Abstract:
In this paper, we present a statistical approach to Language Understanding that allows to avoid the effort of obtaining new semantic models when changing the language. This way, it is not necessary to acquire and label new training corpora in the new language. Our approach consists of learning all the semantic models in a target language and to do the semantic decoding of the sentences pronounced in the source language after a translation process. In order to deal with the errors and the lack of coverage of the translations, a mechanism to generalize the result of several translators is proposed. The graph of words generated in this phase is the input to the semantic decoding algorithm specifically designed to combine statistical models and graphs of words. Some experiments that show the good behavior of the proposed approach are also presented. ; Calvo Lance, M.; Hurtado Oliver, LF.; García Granada, F.; Sanchís Arnal, E. (2012). A multilingual SLU system based on semantic decoding of graphs of words. En Advances in Speech and Language Technologies for Iberian Languages. Springer Verlag (Germany). 328:158-167. doi:10.1007/978-3-642-35292-8_17 ; S ; 158 ; 167 ; 328 ; Hahn, S., Dinarelli, M., Raymond, C., Lefèvre, F., Lehnen, P., De Mori, R., Moschitti, A., Ney, H., Riccardi, G.: Comparing stochastic approaches to spoken language understanding in multiple languages. IEEE Transactions on Audio, Speech, and Language Processing 6(99), 1569–1583 (2010) ; Raymond, C., Riccardi, G.: Generative and discriminative algorithms for spoken language understanding. In: Proceedings of Interspeech 2007, pp. 1605–1608 (2007) ; Tur, G., Mori, R.D.: Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, 1st edn. Wiley (2011) ; Maynard, H.B., Lefèvre, F.: Investigating Stochastic Speech Understanding. In: Proc. of IEEE Automatic Speech Recognition and Understanding Workshop, ASRU (2001) ; Segarra, E., Sanchis, E., Galiano, M., García, F., Hurtado, L.: Extracting Semantic Information Through Automatic Learning Techniques. IJPRAI 16(3), 301–307 (2002) ; He, Y., Young, S.: Spoken language understanding using the hidden vector state model. Speech Communication 48, 262–275 (2006) ; De Mori, R., Bechet, F., Hakkani-Tur, D., McTear, M., Riccardi, G., Tur, G.: Spoken language understanding: A survey. IEEE Signal Processing Magazine 25(3), 50–58 (2008) ; Hakkani-Tür, D., Béchet, F., Riccardi, G., Tur, G.: Beyond ASR 1-best: Using word confusion networks in spoken language understanding. Computer Speech & Language 20(4), 495–514 (2006) ; Tur, G., Wright, J., Gorin, A., Riccardi, G., Hakkani-Tür, D.: Improving spoken language understanding using word confusion networks. In: Proceedings of the ICSLP. Citeseer (2002) ; Tur, G., Hakkani-Tür, D., Schapire, R.E.: Combining active and semi-supervised learning for spoken language understanding. Speech Communication 45, 171–186 (2005) ; Ortega, L., Galiano, I., Hurtado, L.F., Sanchis, E., Segarra, E.: A statistical segment-based approach for spoken language understanding. In: Proc. of InterSpeech 2010, Makuhari, Chiba, Japan, pp. 1836–1839 (2010) ; Sim, K.C., Byrne, W.J., Gales, M.J.F., Sahbi, H., Woodland, P.C.: Consensus network decoding for statistical machine translation system combination. In: IEEE Int. Conference on Acoustics, Speech, and Signal Processing (2007) ; Bangalore, S., Bordel, G., Riccardi, G.: Computing Consensus Translation from Multiple Machine Translation Systems. In: Proceedings of IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2001, pp. 351–354 (2001) ; Larkin, M.A., Blackshields, G., Brown, N.P., Chenna, R., McGettigan, P.A., McWilliam, H., Valentin, F., Wallace, I.M., Wilm, A., Lopez, R., Thompson, J.D., Gibson, T.J., Higgins, D.G.: ClustalW and ClustalX version 2.0. Bioinformatics 23(21), 2947–2948 (2007) ; Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., López de Letona, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: Proceedings of LREC 2006, Genoa, Italy, pp. 1636–1639 (May 2006)
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Keyword:
Graph of Words; LENGUAJES Y SISTEMAS INFORMATICOS; Multilingual Language Understanding
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URL: https://doi.org/10.1007/978-3-642-35292-8_17 http://hdl.handle.net/10251/35379
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Language modelization and categorization for voice-activated QA
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