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

Authors

  • Zhao Meng ETH Zurich
  • Lili Mou University of Waterloo
  • Zhi Jin Peking University

Keywords:

Dialog systems, Speaker modeling

Abstract

In this paper, we address the problem of speaker classification in multi-party conversation, 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.

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Published

2018-04-29

How to Cite

Meng, Z., Mou, L., & Jin, Z. (2018). Towards Neural Speaker Modeling in Multi-Party Conversation: The Task, Dataset, and Models. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12140