Getting To Know You: User Attribute Extraction from Dialogues

Chien-Sheng Wu, Andrea Madotto, Zhaojiang Lin, Peng Xu, Pascale Fung


Abstract
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.
Anthology ID:
2020.lrec-1.73
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
581–589
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.73
DOI:
Bibkey:
Cite (ACL):
Chien-Sheng Wu, Andrea Madotto, Zhaojiang Lin, Peng Xu, and Pascale Fung. 2020. Getting To Know You: User Attribute Extraction from Dialogues. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 581–589, Marseille, France. European Language Resources Association.
Cite (Informal):
Getting To Know You: User Attribute Extraction from Dialogues (Wu et al., LREC 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/2020.lrec-1.73.pdf
Code
 jasonwu0731/GettingToKnowYou