  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 a fine-tuned pre-trained BERT model on our problem.
Based on the evaluation by Mean-Squared-Error (MSE), the model achieved a value
of 0.046 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.
