# Lifelong Knowledge Learning in Rule-based Dialogue Systems

arXiv: https://arxiv.org/abs/2011.09811 · HTML: https://mlsanity.com/paper/2011.09811/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [UER: A Heuristic Bias Addressing Approach for Online Continual Learning](https://mlsanity.com/paper/2309.04081/) (2023)
- [DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines](https://mlsanity.com/paper/2212.10557/) (2022)
- [Talking with Machines: A Comprehensive Survey of Emergent Dialogue Systems](https://mlsanity.com/paper/2305.16324/) (2023)
- [Bil-DOS: A Bi-lingual Dialogue Ordering System (for Subway)](https://mlsanity.com/paper/2210.05773/) (2022)

## Abstract (arXiv)

One of the main weaknesses of current chatbots or dialogue systems is that they do not learn online during conversations after they are deployed. This is a major loss of opportunity. Clearly, each human user has a great deal of knowledge about the world that may be useful to others. If a chatbot can learn from their users during chatting, it will greatly expand its knowledge base and serve its users better. This paper proposes to build such a learning capability in a rule-based chatbot so that it can continuously acquire new knowledge in its chatting with users. This work is useful because many real-life deployed chatbots are rule-based.