# Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals

arXiv: https://arxiv.org/abs/2107.08329 · HTML: https://mlsanity.com/paper/2107.08329/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [A Knowledge-Grounded Neural Conversation Model](https://mlsanity.com/paper/1702.01932/) (2017)
- [Towards Conversational Recommendation over Multi-Type Dialogs](https://mlsanity.com/paper/2005.03954/) (2020)
- [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://mlsanity.com/paper/1906.05572/) (2019)
- [Learning to Select Knowledge for Response Generation in Dialog Systems](https://mlsanity.com/paper/1902.04911/) (2019)
- [Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/1905.11553/) (2019)
- [Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue](https://mlsanity.com/paper/1909.03922/) (2019)
- [Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading](https://mlsanity.com/paper/1906.02738/) (2019)
- [Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/2002.01196/) (2020)
- [ScriptWriter: Narrative-Guided Script Generation](https://mlsanity.com/paper/2005.10331/) (2020)
- [Content Selection Network for Document-grounded Retrieval-based Chatbots](https://mlsanity.com/paper/2101.08426/) (2021)

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

- [Large Language Models for Information Retrieval: A Survey](https://mlsanity.com/paper/2308.07107/) (2023)
- [Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy](https://mlsanity.com/paper/2204.07433/) (2022)
- [Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot](https://mlsanity.com/paper/2108.07935/) (2021)
- [MoocRadar: A Fine-grained and Multi-aspect Knowledge Repository for Improving Cognitive Student Modeling in MOOCs](https://mlsanity.com/paper/2304.02205/) (2023)
- [Target-Guided Open-Domain Conversation Planning](https://mlsanity.com/paper/2209.09746/) (2022)
- [Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling](https://mlsanity.com/paper/2412.02415/) (2024)
- [Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning](https://mlsanity.com/paper/2211.15359/) (2022)
- [A Conditional Generative Chatbot using Transformer Model](https://mlsanity.com/paper/2306.02074/) (2023)
- [ProMediConv: Benchmarking Proactive Conversational Agents in Legal Dispute Mediation](https://mlsanity.com/paper/2609.11101/) (2026)

## Abstract (arXiv)

A proactive dialogue system has the ability to proactively lead the conversation. Different from the general chatbots which only react to the user, proactive dialogue systems can be used to achieve some goals, e.g., to recommend some items to the user. Background knowledge is essential to enable smooth and natural transitions in dialogue. In this paper, we propose a new multi-task learning framework for retrieval-based knowledge-grounded proactive dialogue. To determine the relevant knowledge to be used, we frame knowledge prediction as a complementary task and use explicit signals to supervise its learning. The final response is selected according to the predicted knowledge, the goal to achieve, and the context. Experimental results show that explicit modeling of knowledge prediction and goal selection can greatly improve the final response selection. Our code is available at https://github.com/DaoD/KPN/.