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Predicting Subjective Features of Questions of QA Websites using BERT
Issa Annamoradnejad Mohammadamin Fazli Jafar Habibi
Abstract
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.
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| common-sense-reasoning-on-crowdsource-qa-1 | BERT | MSE: 0.046 |
| community-question-answering-on-crowdsource-1 | BERT | MSE: 0.046 |
| question-quality-assessment-on-crowdsource-qa-1 | BERT | MSE: 0.046 |
| reading-comprehension-on-crowdsource-qa-1 | BERT | MSE: 0.046 |
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