  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 labeled utterances are required for model
training; (2) personal attribute knowledge embedded in both utterances and
external resources is underutilized; (3) the performance on predicting some
difficult personal attributes is unsatisfactory. In this paper, we propose a
framework DSCGN based on the pre-trained language model with a noise-robust
loss function to predict personal attributes from conversations without
requiring any labeled utterances. We yield two categories of supervision, i.e.,
document-level supervision via a distant supervision strategy and
contextualized word-level supervision via a label guessing method, by mining
the personal attribute knowledge embedded in both unlabeled utterances and
external resources to fine-tune the language model. Extensive experiments over
two real-world data sets (i.e., a profession data set and a hobby data set)
show our framework obtains the best performance compared with all the twelve
baselines in terms of nDCG and MRR.
