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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.

References

  1. Brown, T.B., Mann, B., Ryder, N., Subbiah, M., et al.: Language models are few-shot learners (2020)

    Google Scholar 

  2. Cassell, J., Sullivan, J., Churchill, E., Prevost, S.: Embodied Conversational Agents. MIT Press (2000)

    Google Scholar 

  3. Clark, L., Pantidi, N., Cooney, O., Doyle, P., Garaialde, D., Edwards, J., Spillane, B., Gilmartin, E., Murad, C., Munteanu, C., Wade, V., Cowan, B.R.: What makes a good conversation? Challenges in designing truly conversational agents. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, pp. 1–12. CHI ’19 (2019)

    Google Scholar 

  4. Clavel, C., Callejas, Z.: Sentiment analysis: from opinion mining to human-agent interaction. IEEE Trans. Affect. Comput. 7(1), 74–93 (2016)

    Article  Google Scholar 

  5. Graterol, W., Diaz-Amado, J., Cardinale, Y., Dongo, I., Lopes-Silva, E., Santos-Libarino, C.: Emotion detection for social robots based on NLP transformers and an emotion ontology (2021)

    Google Scholar 

  6. Hameed, I.A.: Using natural language processing (NLP) for designing socially intelligent robots. In: 2016 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (2016)

    Google Scholar 

  7. Klingspor, V., Demiris, J., Kaiser, M.: Human-robot-communication and machine learning, p. 41

    Google Scholar 

  8. Laranjo, L., Dunn, A.G., Tong, H.L., Kocaballi, A.B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A.Y.S., Coiera, E.: Conversational agents in healthcare: a systematic review. J. Am. Med. Inf. Assoc. 25(9), 1248–1258 (2018)

    Article  Google Scholar 

  9. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training, p. 12

    Google Scholar 

  10. Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv:1910.10683 [cs, stat] (2020)

  11. Rheu, M., Shin, J.Y., Peng, W., Huh-Yoo, J.: Systematic review: trust-building factors and implications for conversational agent design. Int. J. Human-Comput. Int. 37(1), 81–96 (2021)

    Article  Google Scholar 

  12. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to Sequence Learning with Neural Networks. In: Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc. (2014)

    Google Scholar 

  13. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need (2017)

    Google Scholar 

  14. Weiss, K., Khoshgoftaar, T.M., Wang, D.D.: A survey of transfer learning. J. Big Data 3(1), 1–40 (2016). https://doi.org/10.1186/s40537-016-0043-6

    Article  Google Scholar 

  15. Woo, J., Botzheim, J., Kubota, N.: Conversation system for natural communication with robot partner. In: 2014 10th France-Japan/8th Europe-Asia Congress on Mechatronics (MECATRONICS2014- Tokyo) (2014)

    Google Scholar 

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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).

Author information

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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