Abstract
We present here two experiments carried out to improve the performances of an intelligent human-machine interface, called UKKO. The interface is destined to be implemented in humanoid social robots. From the standpoint of the automatic generation of outgoing messages, the way the interface works is not repetitive but creative. In other words, it does not reproduce talking points extracted from corpus but produces its own utterances by using rules. These rules exploit quality linguistic resources, i.e., formalized descriptions of lexicon that concern its morphological, syntactic and semantic features. The system’s quality of speaking depends on the completeness of descriptions inserted in its database. We discuss an experimentation that uses deep learning techniques to automatically retrieve syntactic and semantic properties of lexical units, which can be inserted into the database. An unsupervised method, based on the Word2Vec algorithm, is used for the semantic properties. The first results obtained are promising and show the interest of this approach for the experimentation. A supervised method, based on a sequential algorithm, is used for the syntactic properties. This method requires the transformation of the original data to match the training data that come from the UKKO system. The initial results achieved need to be improved. A line of research would be the addition of semantic descriptors to syntactic descriptors so as to make the approach achieve a better performance. This is consistent with the linguistic modeling underlying this project.
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References
Buvet, P.-A., Fache, V., Rouam, A.: How does a robot speak? About the man-machineverbal interaction. In: Kuc, T.-Y., Manzoor, S., Tiddi, I., Masoumeh, M., Bastianelli, E., and Gyrard, A. (eds.) The 3rd International Workshop on the Applications of Knowledge Representation and Semantic Technologies in Robotics. CEUR, Macau (2019)
Buvet, P.-A., Fache, B., Rouam, A.: Interview with a robot: how to equip the elderly companion robots with speech? In: Arai, K., Kapoor, S., Bhatia, R. (eds.) FTC 2020. AISC, vol. 1289, pp. 310–326. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-63089-8_20
Buvet, P.-A.: Linguistique et intelligence. In: Linguistique et. Peter Lang (in press)
Landragin, F.: Comment parle un robot ?: les machines à langage dans la science-fiction. (2020)
Kerbrat-Orecchioni, C.: La conversation. Seuil, Paris (1996)
Tisseron, S.: Le jour où mon robot m’aimera: vers l’empathie artificielle. Albin Michel, Paris (2015)
Buvet, P.-A.: La dimension lexicale de la détermination en français. Honoré Champion, Paris (2013)
Mayaffre, D.: Plaidoyer en faveur de l’Analyse de Données co(n)Textuelles. Parcours cooccurrentiels dans le discours présidentiel français (1958–2014). In: Presented at the, Paris (2014)
Gillot, C.: Modèles de langue exploitant la similarité structurelle entre séquences pour la reconnaissance de la parole. https://tel.archives-ouvertes.fr/tel-01258153 (2012)
Gross, M.: Une grammaire locale de l’expression des sentiments. Lang. Fr. 105, 70–87 (1995). https://doi.org/10.3406/lfr.1995.5294
Courtois, B.: Un système de dictionnaires électroniques pour les mots simples du français. Lang. Fr. 87, 11–22 (1990). https://doi.org/10.3406/lfr.1990.6323
Silberztein, M.: Formalizing natural languages: the Nooj approach. ISTE Ltd; John Wiley & Sons Inc, London (2016)
Blanco, X.: Valeurs grammaticales et structures prédicat-argument. Langages 176, 50 (2009). https://doi.org/10.3917/lang.176.0050
Fabre, C.: Sémantique distributionnelle automatique : la proximité distributionnelle comme mode d’accès au sens. É Études Linguist. Appliquée. No 180, 395 (2015). https://doi.org/10.3917/ela.180.0395
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. CoRR. abs/1301.3781 (2013)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed Representations of Words and Phrases and their Compositionality. In: Burges, C.J.C., Bottou, L., Welling, M., Ghahramani, Z., and Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems. pp. 3111–3119. Curran Associates, Inc (2013)
Laude, H.: TensorFlow et Keras: l’intelligence artificielle appliquée à la robotique humanoïde. ENI, Saint-Herblain (2019)
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Buvet, PA., Fache, B., Rouam, A., Fadel, W. (2022). Which Intelligence for Human-Machine Dialogue Systems?. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2021, Volume 1. FTC 2021. Lecture Notes in Networks and Systems, vol 358. Springer, Cham. https://doi.org/10.1007/978-3-030-89906-6_10
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