Global Thread-Level Inference for Comment Classification in Community Question Answering
This work addresses the need for better answer quality assessment in community question answering platforms, though it is incremental as it builds on existing methods with thread-level enhancements.
The paper tackled the problem of automatically classifying good and bad answers in community question answering by exploiting thread-level structure and relations between comments, achieving improved results over the state of the art on the SemEval-2015 Task 3 benchmark dataset.
Community question answering, a recent evolution of question answering in the Web context, allows a user to quickly consult the opinion of a number of people on a particular topic, thus taking advantage of the wisdom of the crowd. Here we try to help the user by deciding automatically which answers are good and which are bad for a given question. In particular, we focus on exploiting the output structure at the thread level in order to make more consistent global decisions. More specifically, we exploit the relations between pairs of comments at any distance in the thread, which we incorporate in a graph-cut and in an ILP frameworks. We evaluated our approach on the benchmark dataset of SemEval-2015 Task 3. Results improved over the state of the art, confirming the importance of using thread level information.