Personal Attribute Prediction from Conversations

@article{Liu2022PersonalAP,
  title={Personal Attribute Prediction from Conversations},
  author={Yinan Liu and Hu Chen and Wei Shen},
  journal={Companion Proceedings of the Web Conference 2022},
  year={2022}
}
Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation. Conversations containing rich personal knowledge can be regarded as a main source to populate the PKB. Given a user, a user attribute, and user utterances from a conversational system, we aim to predict the personal attribute value for the user, which is helpful for the enrichment of PKBs. However, there are three issues existing in previous studies: (1) manually… 
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Low-resource Personal Attribute Prediction from Conversation

A novel framework PEARL is proposed to predict personal attributes from conversations by leveraging the abun- dant personal attribute knowledge from utterances under a low-resource setting in which no labeled utterances or exter- nal data are utilized.

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