PEPT: Expert Finding Meets Personalized Pre-training
CoRR(2023)
摘要
Finding appropriate experts is essential in Community Question Answering
(CQA) platforms as it enables the effective routing of questions to potential
users who can provide relevant answers. The key is to personalized learning
expert representations based on their historical answered questions, and
accurately matching them with target questions. There have been some
preliminary works exploring the usability of PLMs in expert finding, such as
pre-training expert or question representations. However, these models usually
learn pure text representations of experts from histories, disregarding
personalized and fine-grained expert modeling. For alleviating this, we present
a personalized pre-training and fine-tuning paradigm, which could effectively
learn expert interest and expertise simultaneously. Specifically, in our
pre-training framework, we integrate historical answered questions of one
expert with one target question, and regard it as a candidate aware
expert-level input unit. Then, we fuse expert IDs into the pre-training for
guiding the model to model personalized expert representations, which can help
capture the unique characteristics and expertise of each individual expert.
Additionally, in our pre-training task, we design: 1) a question-level masked
language model task to learn the relatedness between histories, enabling the
modeling of question-level expert interest; 2) a vote-oriented task to capture
question-level expert expertise by predicting the vote score the expert would
receive. Through our pre-training framework and tasks, our approach could
holistically learn expert representations including interests and expertise.
Our method has been extensively evaluated on six real-world CQA datasets, and
the experimental results consistently demonstrate the superiority of our
approach over competitive baseline methods.
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