Detecting Speaker Personas from Conversational Texts
Jia-Chen Gu, Zhenhua Ling, Yu Wu, Quan Liu, Zhigang Chen, Xiaodan Zhu
Abstract
Personas are useful for dialogue response prediction. However, the personas used in current studies are pre-defined and hard to obtain before a conversation. To tackle this issue, we study a new task, named Speaker Persona Detection (SPD), which aims to detect speaker personas based on the plain conversational text. In this task, a best-matched persona is searched out from candidates given the conversational text. This is a many-to-many semantic matching task because both contexts and personas in SPD are composed of multiple sentences. The long-term dependency and the dynamic redundancy among these sentences increase the difficulty of this task. We build a dataset for SPD, dubbed as Persona Match on Persona-Chat (PMPC). Furthermore, we evaluate several baseline models and propose utterance-to-profile (U2P) matching networks for this task. The U2P models operate at a fine granularity which treat both contexts and personas as sets of multiple sequences. Then, each sequence pair is scored and an interpretable overall score is obtained for a context-persona pair through aggregation. Evaluation results show that the U2P models outperform their baseline counterparts significantly.
BibTeX
@inproceedings{gu-etal-2021-detecting,
title = "Detecting Speaker Personas from Conversational Texts",
author = "Gu, Jia-Chen and
Ling, Zhenhua and
Wu, Yu and
Liu, Quan and
Chen, Zhigang and
Zhu, Xiaodan",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.86/",
doi = "10.18653/v1/2021.emnlp-main.86",
pages = "1126--1136"
}