# Learning Conversational Systems that Interleave Task and Non-Task Content

arXiv: https://arxiv.org/abs/1703.00099 · HTML: https://mlsanity.com/paper/1703.00099/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

## Built on by (most cited first, 7 of 7)

- [Survey on reinforcement learning for language processing](https://mlsanity.com/paper/2104.05565/) (2021)
- [SalesBot: Transitioning from Chit-Chat to Task-Oriented Dialogues](https://mlsanity.com/paper/2204.10591/) (2022)
- [Multi-User Chat Assistant (MUCA): a Framework Using LLMs to Facilitate Group Conversations](https://mlsanity.com/paper/2401.04883/) (2024)
- [HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management](https://mlsanity.com/paper/2105.06041/) (2021)
- [DEUX: An Attribute-Guided Framework for Sociable Recommendation Dialog Systems](https://mlsanity.com/paper/2105.00825/) (2021)
- [End-to-End Task-Oriented Dialog Modeling with Semi-Structured Knowledge Management](https://mlsanity.com/paper/2106.11796/) (2021)
- [Enhancing User Engagement in Socially-Driven Dialogue through Interactive LLM Alignments](https://mlsanity.com/paper/2506.21497/) (2025)

## Abstract (arXiv)

Task-oriented dialog systems have been applied in various tasks, such as automated personal assistants, customer service providers and tutors. These systems work well when users have clear and explicit intentions that are well-aligned to the systems' capabilities. However, they fail if users intentions are not explicit. To address this shortcoming, we propose a framework to interleave non-task content (i.e. everyday social conversation) into task conversations. When the task content fails, the system can still keep the user engaged with the non-task content. We trained a policy using reinforcement learning algorithms to promote long-turn conversation coherence and consistency, so that the system can have smooth transitions between task and non-task content. To test the effectiveness of the proposed framework, we developed a movie promotion dialog system. Experiments with human users indicate that a system that interleaves social and task content achieves a better task success rate and is also rated as more engaging compared to a pure task-oriented system.