AUnet: An Unsupervised Method for Answer Reliability Evaluation in Community QA Systems

Recently, cQA websites such as Baidu Zhidao and StackExchange have exploded in popularity since everyone can post questions for other users to answer which fully realize the value of exchange. Nevertheless, the answers from different users for a same question may include errors, irrelevant messages or malicious advertisements due to the great different backgrounds of users. Hence, the automatic method for answer reliability evaluation is very important for improving users’ experience. However, the weakness of existing supervised methods is the high cost for they need a lot of annotated data. To alleviate such problems, we proposed a novel unsupervised answer evaluation method exploiting Answer-User association Network in this paper. Based on the constructed network, the reliability of answers and users can be obtained simultaneously by an iterative process. The experimental results on real word datasets show that our proposed method outperforms existing approaches.