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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 57506 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Beigy, Hamid
- Abstract:
- Expert finding is an important task in community question answering (CQA) websites, enabling the routing of new questions towards users who have the highest level of expertise in the relevant topic. This method helps question raisers receive satisfactory responses in a shorter time and makes it easier for answerers to find questions they are interested in and have enough expertise to answer. . The primary goal in expert finding is to learn the representation of questions and expert candidates based on the history of answered questions. Many existing approaches generate a unique representation for users without considering the specific question asked. Additionally, many of these approaches only consider users' interest in answering questions and overlook their expertise. While deep learning-based approaches have significantly outperformed traditional methods, they face the challenge of high computational complexity, making the process of estimating expertise for all active community users time-consuming. To address these challenges, a model based on pre-trained language models is proposed, which: 1) learns the existing knowledge in CQA websites by introducing a pre-training paradigm, 2) enables the retrieval of questions similar to the new question from the expert candidate's history through a fine-tuning stage on duplicate questions, 3) provides an architecture for predicting expertise levels by learning the match between the new question and the user's question history based on question titles and tags, and 4) introduces a two-stage retrieval-re-ranking approach, where a model with lower computational complexity based on a multi-encoder is used for retrieving potential expert candidates, followed by re-ranking the retrieved candidates using a re-ranking model. The results of experiments conducted on four CQA datasets demonstrate that the proposed model has achieved average improvements of 1% and 1% in MRR and P@3 metrics, respectively, compared to the best baseline models
- Keywords:
- Expert Finding ; Community Question Answering ; Language Model ; Pretrained Models
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