# Dialog Policy Learning for Joint Clarification and Active Learning Queries

arXiv: https://arxiv.org/abs/2006.05456 · HTML: https://mlsanity.com/paper/2006.05456/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [Simple Baselines for Interactive Video Retrieval with Questions and Answers](https://mlsanity.com/paper/2308.10402/) (2023)
- [Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval with Partial Query](https://mlsanity.com/paper/2103.01654/) (2021)
- [Deciding Whether to Ask Clarifying Questions in Large-Scale Spoken Language Understanding](https://mlsanity.com/paper/2109.12451/) (2021)
- [What Does The User Want? Information Gain for Hierarchical Dialogue Policy Optimisation](https://mlsanity.com/paper/2109.07129/) (2021)
- [Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables](https://mlsanity.com/paper/2502.09073/) (2025)
- [Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization](https://mlsanity.com/paper/2305.03262/) (2023)
- [Goal-Guided Efficient Exploration via Large Language Model in Reinforcement Learning](https://mlsanity.com/paper/2509.22008/) (2025)
- [Closing the Expression Gap in LLM Instructions via Socratic Questioning](https://mlsanity.com/paper/2510.27410/) (2025)

## Abstract (arXiv)

Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the use of clarifications for correction and resolving uncertainty, and active learning queries to learn new concepts encountered during operation. Prior work on dialog systems has either focused on exclusively learning how to perform clarification/ information seeking, or to perform active learning. In this work, we train a hierarchical dialog policy to jointly perform both clarification and active learning in the context of an interactive language-based image retrieval task motivated by an online shopping application, and demonstrate that jointly learning dialog policies for clarification and active learning is more effective than the use of static dialog policies for one or both of these functions.