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Towards User-Centric Text-to-Text Generation: A Survey

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Text, Speech, and Dialogue (TSD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12848))

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Abstract

Natural Language Generation (NLG) has received much attention with rapidly developing models and ever-more available data. As a result, a growing amount of work attempts to personalize these systems for better human interaction experience. Still, diverse sets of research across multiple dimensions and numerous levels of depth exist and are scattered across various communities. In this work, we survey the ongoing research efforts and introduce a categorization of these under the umbrella user-centric natural language generation. We further discuss some of the challenges and opportunities in NLG personalization.

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Notes

  1. 1.

    In this work, NLG mainly refers to text-to-text generation.

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Authors and Affiliations

  1. School of Interactive Computing, Georgia Institute of Technology, Atlanta, USA

    Diyi Yang

  2. Conversational AI and Social Analytics (CAISA) Lab, Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany

    Lucie Flek

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    Kamil Ekštein

  2. University of West Bohemia, Pilsen, Czech Republic

    František Pártl

  3. University of West Bohemia, Pilsen, Czech Republic

    Miloslav Konopík

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Yang, D., Flek, L. (2021). Towards User-Centric Text-to-Text Generation: A Survey. In: Ekštein, K., Pártl, F., Konopík, M. (eds) Text, Speech, and Dialogue. TSD 2021. Lecture Notes in Computer Science(), vol 12848. Springer, Cham. https://doi.org/10.1007/978-3-030-83527-9_1

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