# When to Talk: Chatbot Controls the Timing of Talking during Multi-turn Open-domain Dialogue Generation

arXiv: https://arxiv.org/abs/1912.09879 · HTML: https://mlsanity.com/paper/1912.09879/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Meta-evaluation of Conversational Search Evaluation Metrics](https://mlsanity.com/paper/2104.13453/) (2021)
- [POSSCORE: A Simple Yet Effective Evaluation of Conversational Search with Part of Speech Labelling](https://mlsanity.com/paper/2109.03039/) (2021)
- [A Static and Dynamic Attention Framework for Multi Turn Dialogue Generation](https://mlsanity.com/paper/2410.20766/) (2024)

## Abstract (arXiv)

Despite the multi-turn open-domain dialogue systems have attracted more and more attention and made great progress, the existing dialogue systems are still very boring. Nearly all the existing dialogue models only provide a response when the user's utterance is accepted. But during daily conversations, humans always decide whether to continue to utter an utterance based on the context. Intuitively, a dialogue model that can control the timing of talking autonomously based on the conversation context can chat with humans more naturally. In this paper, we explore the dialogue system that automatically controls the timing of talking during the conversation. Specifically, we adopt the decision module for the existing dialogue models. Furthermore, modeling conversation context effectively is very important for controlling the timing of talking. So we also adopt the graph neural networks to process the context with the natural graph structure. Extensive experiments on two benchmarks show that controlling the timing of talking can effectively improve the quality of dialogue generation, and the proposed methods significantly improve the accuracy of the timing of talking. In addition, we have publicly released the codes of our proposed model.