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Cross-Domain Polarity Models to Evaluate User eXperience in E-learning
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Resum abstractiu de noticies basat en xarxes neuronals ; Resumen abstractivo de noticias basado en redes neuronales
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Aprendizaje automático para la detección de humor en Twitter
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Extractive summarization using siamese hierarchical transformer encoders
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Transformer based contextualization of pre-trained word embeddings for irony detection in Twitter
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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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Choosing the right loss function for multi-label Emotion Classification
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Creación de un corpus de artículos de prensa y generación automática de resúmenes
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Análisis de sentimientos a nivel de aspecto usando ontologías y aprendizaje automático ; Aspect-based sentiment analysis using ontologies and machine learning
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Aprendizaje profundo para el procesamiento del lenguaje natural
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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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| 15 |
A Train-on-Target Strategy for Multilingual Spoken Language Understanding
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Language identification of multilingual posts from Twitter: a case study
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Multilingual Spoken Language Understanding using graphs and multiple translations
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| 19 |
Combining Several ASR Outputs in a Graph-Based SLU System
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Abstract:
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-25751-8_66 ; In this paper, we present an approach to Spoken Language Understanding (SLU) where we perform a combination of multiple hypotheses from several Automatic Speech Recognizers (ASRs) in order to reduce the impact of recognition errors in the SLU module. This combination is performed using a Grammatical Inference algorithm that provides a generalization of the input sentences by means of a weighted graph of words. We have also developed a specific SLU algorithm that is able to process these graphs of words according to a stochastic semantic modelling.The results show that the combinations of several hypotheses from the ASR module outperform the results obtained by taking just the 1-best transcription ; This work is partially supported by the Spanish MEC under contract TIN2014-54288-C4-3-R and FPU Grant AP2010-4193. ; Calvo Lance, M.; Hurtado Oliver, LF.; García-Granada, F.; Sanchís Arnal, E. (2015). Combining Several ASR Outputs in a Graph-Based SLU System. En Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer. 551-558. https://doi.org/10.1007/978-3-319-25751-8_66 ; S ; 551 ; 558 ; Bangalore, S., Bordel, G., Riccardi, G.: Computing consensus translation from multiple machine translation systems. In: ASRU, pp. 351–354 (2001) ; Benedí, J.M., Lleida, E., Varona, A., Castro, M.J., Galiano, I., Justo, R., de Letona, I.L., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: LREC, pp. 1636–1639 (2006) ; Bonneau-Maynard, H., Lefèvre, F.: Investigating stochastic speech understanding. In: IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp. 260–263 (2001) ; Calvo, M., García, F., Hurtado, L.F., Jiménez, S., Sanchis, E.: Exploiting multiple hypotheses for multilingual spoken language understanding. In: CoNLL, pp. 193–201 (2013) ; Fiscus, J.G.: A post-processing system to yield reduced word error rates: recognizer output voting error reduction (ROVER). In: 1997 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 347–354 (1997) ; 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) ; Hakkani-Tür, D., Béchet, F., Riccardi, G., Tür, G.: Beyond ASR 1-best: Using word confusion networks in spoken language understanding. Computer Speech & Language 20(4), 495–514 (2006) ; He, Y., Young, S.: Spoken language understanding using the hidden vector state model. Speech Communication 48, 262–275 (2006) ; 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) ; Segarra, E., Sanchis, E., Galiano, M., García, F., Hurtado, L.: Extracting Semantic Information Through Automatic Learning Techniques. IJPRAI 16(3), 301–307 (2002) ; Tür, G., Deoras, A., Hakkani-Tür, D.: Semantic parsing using word confusion networks with conditional random fields. In: INTERSPEECH (2013)
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Keyword:
Graph of concepts; Graph of words; LENGUAJES Y SISTEMAS INFORMATICOS; Spoken language understanding
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URL: http://hdl.handle.net/10251/66425
https://doi.org/10.1007/978-3-319-25751-8_66
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