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Real-Time Skill Discovery in Intelligent Virtual Assistants

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Advances in Knowledge Discovery and Data Mining (PAKDD 2022)

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Abstract

Solution to many real-world problems often involve the use of expert-level knowledge from various specializations. Such inter-disciplinary problems are usually divided into tasks which are then assigned to a set of bots, each specialized in a particular skill. Supervised selection of the right bot each time is cumbersome and not scalable. Hence there is a need for an AI system that identifies the type of task and assigns it to a suitably trained bot. Challenges arise in non-stationary environments when the cost of choosing different bots vary or the bots themselves might evolve in their skills. In this paper, as in Conversational AI, a number of bots are at our disposal, each of which is trained to handle (i.e., answer) a specific type of question in a conversation. We develop a meta-algorithm that learns about capabilities (Skill Discovery) of the available bots in real-time and appropriately selects a relevant bot for the question at hand. We present contextual bandits as a solution in this setting and introduce gradual finetuning of query information to improve Skill Discovery. Using two popular datasets from conversational AI: CoQA and SQuAD, we show promising results of our method on non-stationary environments.

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Notes

  1. 1.

    We used ‘BERT-base-uncased’ model from Hugging Face.

References

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Acknowledgement

This research was partially funded by the Australian Government through the Australian Research Council (ARC). Prof. Venkatesh is the recipient of an ARC Australian Laureate Fellowship (FL170100006).

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Authors and Affiliations

  1. Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia

    Preeti Gopal, Sunil Gupta, Santu Rana, Vuong Le, Trong Nguyen & Svetha Venkatesh

Authors
  1. Preeti Gopal
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  2. Sunil Gupta
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  3. Santu Rana
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  4. Vuong Le
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  5. Trong Nguyen
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  6. Svetha Venkatesh
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Corresponding author

Correspondence to Preeti Gopal .

Editor information

Editors and Affiliations

  1. Laboratory of Artificial Intelligence and Decision Support, University of Porto, Porto, Portugal

    João Gama

  2. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Tianrui Li

  3. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Yang Yu

  4. School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

    Enhong Chen

  5. JD iCity, JD Technology & JD Intelligent Cities Research, Beijing, China

    Yu Zheng

  6. School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China

    Fei Teng

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Gopal, P., Gupta, S., Rana, S., Le, V., Nguyen, T., Venkatesh, S. (2022). Real-Time Skill Discovery in Intelligent Virtual Assistants. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. Lecture Notes in Computer Science(), vol 13280. Springer, Cham. https://doi.org/10.1007/978-3-031-05933-9_25

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  • DOI: https://doi.org/10.1007/978-3-031-05933-9_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05932-2

  • Online ISBN: 978-3-031-05933-9

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