Community question answering is a forum in which users ask their questions and other existing users respond those questions. As these forums grow, their users, questions and answers increase significantly and new challenges appear. For instance, someone who has asked a question in forum has to wait a lot to another user give him a response. On the other, experts has to spend a lot of time finding questions in their expertise field. Thus, the most important challenge which these forums face is finding appropriate users to answer the questions. One of the most critical issues which expert finding algorithms have is vocabulary gap between skills and words used in posts text. Therefore, in this paper, we propose two translation models to reduce vocabulary gap. The proposed models are evaluated using the StackOverflow dataset and compared with the related algorithms. The experimental results show the superiority of the proposed models.

Community Question-Answering websites, such as StackOverflow and Quora, expect users to follow specific guidelines in order to maintain content quality. These systems mainly rely on community reports for assessing contents, which has serious problems, such as the slow handling of violations, the loss of normal and experienced users' time, the low quality of some reports, and discouraging feedback to new users. Therefore, with the overall goal of providing solutions for automating moderation actions in Q&A websites, we aim to provide a model to predict 20 quality or subjective aspects of questions in QA websites. To this end, we used data gathered by the CrowdSource team at Google Research in 2019 and fine-tuned pre-trained BERT model on our problem. Based on our evaluation, model achieved value of 0. 046 for Mean-Squared-Error (MSE) after 2 epochs of training, which did not improve substantially in the next ones. Results confirm that by simple fine-tuning, we can achieve accurate models in little time and on less amount of data.