# Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models

arXiv: https://arxiv.org/abs/1708.03152 · HTML: https://mlsanity.com/paper/1708.03152/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2017.

## Built on by (most cited first, 8 of 8)

- [MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding](https://mlsanity.com/paper/2106.01541/) (2021)
- [GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding](https://mlsanity.com/paper/2305.09360/) (2023)
- [Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks](https://mlsanity.com/paper/2105.10323/) (2021)
- [A Speaker-aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-turn Dialogue Generation](https://mlsanity.com/paper/2110.06823/) (2021)
- [Revisiting Conversation Discourse for Dialogue Disentanglement](https://mlsanity.com/paper/2306.03975/) (2023)
- [Thread Detection and Response Generation using Transformers with Prompt Optimisation](https://mlsanity.com/paper/2403.05931/) (2024)
- [SS-MPC: A Sequence-Structured Multi-Party Conversation System](https://mlsanity.com/paper/2502.16920/) (2025)
- [Multi-party Response Generation with Relation Disentanglement](https://mlsanity.com/paper/2403.10827/) (2024)

## Abstract (arXiv)

Neural network-based dialog systems are attracting increasing attention in both academia and industry. Recently, researchers have begun to realize the importance of speaker modeling in neural dialog systems, but there lacks established tasks and datasets. In this paper, we propose speaker classification as a surrogate task for general speaker modeling, and collect massive data to facilitate research in this direction. We further investigate temporal-based and content-based models of speakers, and propose several hybrids of them. Experiments show that speaker classification is feasible, and that hybrid models outperform each single component.