User attributes provide rich and useful information for user understanding, yet structured and easy-to-use attributes are often sparsely populated. In this paper, we leverage dialogues with conversational agents, which contain strong suggestions of user information, to automatically extract user attributes. Since no existing dataset is available for this purpose, we apply distant supervision to train our proposed two-stage attribute extractor, which surpasses several retrieval and generation baselines on human evaluation. Meanwhile, we discuss potential applications (e.g., personalized recommendation and dialogue systems) of such extracted user attributes, and point out current limitations to cast light on future work.
@article{arxiv.1908.04621,
title = {Getting To Know You: User Attribute Extraction from Dialogues},
author = {Chien-Sheng Wu and Andrea Madotto and Zhaojiang Lin and Peng Xu and Pascale Fung},
journal= {arXiv preprint arXiv:1908.04621},
year = {2019}
}
Comments
1st Workshop on NLP for Conversational AI @ ACL 2019