Towards Learning Through Open-Domain Dialog
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Abstract (arXiv)
The development of artificial agents able to learn through dialog without domain restrictions has the potential to allow machines to learn how to perform tasks in a similar manner to humans and change how we relate to them. However, research in this area is practically nonexistent. In this paper, we identify the modifications required for a dialog system to be able to learn from the dialog and propose generic approaches that can be used to implement those modifications. More specifically, we discuss how knowledge can be extracted from the dialog, used to update the agent's semantic network, and grounded in action and observation. This way, we hope to raise awareness for this subject, so that it can become a focus of research in the future.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 29 references that are in the map. First posted on arXiv 7 Feb 2022.
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10 of 29 references.
References · 10
- 2016A Network-based End-to-End Trainable Task-oriented Dialogue Systemcited by 252
- 2020UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2cited by 111
- 2018Neural Open Information Extractioncited by 50
- 2017A Deep Reinforcement Learning Chatbotcited by 36
- 2018Anaphora and Coreference Resolution: A Reviewcited by 31
- 2020TicketTalk: Toward human-level performance with end-to-end, transaction-based dialog systemscited by 18
- 2015A Roadmap towards Machine Intelligencecited by 15
- 2019Ensemble-Based Deep Reinforcement Learning for Chatbotscited by 11
- 2020OpenIE6: Iterative Grid Labeling and Coordination Analysis for Open Information Extractioncited by 11
- 2017Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meaningscited by 2
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