Getting To Know You: User Attribute Extraction from Dialogues
This work addresses the need for structured user information in applications like personalized recommendation and dialogue systems, though it is incremental as it builds on existing extraction methods with a new dataset.
The paper tackles the problem of automatically extracting user attributes from dialogues with conversational agents, achieving results that surpass several retrieval and generation baselines on human evaluation.
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.