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User-Adapted Semantic Description Generation Using Natural Language Models

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Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence (ISAmI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 603))

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Abstract

Nowadays, conversational agents are making their way into our lives in many fields. The agent’s speech is an important element in human-computer interaction, and to appear natural and friendly it should avoid predefined texts. With this premise, together with the significant growth in natural language generation models, this work explores the capabilities of natural language models to lead to a more fluent human-computer interaction and open up a range of new opportunities and applications. The present work proposes a system for the generation of descriptions tailored to the user’s profile from different random topics using Natural Language models. After implementing the different applications, integrating natural language generation in conversational agents has proven to be highly effective.

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Notes

  1. 1.

    https://www.britannica.com/science/gravity-physics.

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Acknowledgement

This work was partially supported by project Robots sociales para estimulación física, cognitiva y afectiva de mayores (ROSES) RTI2018-096338-B-I00 funded by Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universidades. This publication is part of the R &D &I project PLEC2021-007819 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR. This work has also been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M (“Fostering Young Doctors Research”, SMM4HRI-CM-UC3M), and in the context of the V PRICIT (Research and Technological Innovation Regional Programme).

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Authors and Affiliations

  1. Robotics Lab, Universidad Carlos III de Madrid, Getafe, Spain

    Javier Sevilla Salcedo, Laura Martín Galván, Jose C. Castillo, Álvaro Castro-González & Miguel A. Salichs

Authors
  1. Javier Sevilla Salcedo
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  2. Laura Martín Galván
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  3. Jose C. Castillo
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  4. Álvaro Castro-González
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  5. Miguel A. Salichs
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Corresponding author

Correspondence to Javier Sevilla Salcedo .

Editor information

Editors and Affiliations

  1. Universitat Politècnica de València, Valencia, Valencia, Spain

    Vicente Julián

  2. ISEP/GECAD, Porto, Portugal

    João Carneiro

  3. Científico Universidad de Salamanca, AIR Institute Villamayor, Parque, Salamanca, Spain

    Ricardo S. Alonso

  4. Edificio I+D+i (24.2), University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

  5. Departamento de Informática, University of Minho, Braga, Portugal

    Paulo Novais

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Sevilla Salcedo, J., Martín Galván, L., Castillo, J.C., Castro-González, Á., Salichs, M.A. (2023). User-Adapted Semantic Description Generation Using Natural Language Models. In: Julián, V., Carneiro, J., Alonso, R.S., Chamoso, P., Novais, P. (eds) Ambient Intelligence—Software and Applications—13th International Symposium on Ambient Intelligence. ISAmI 2022. Lecture Notes in Networks and Systems, vol 603. Springer, Cham. https://doi.org/10.1007/978-3-031-22356-3_13

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