Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
This addresses the problem of selecting relevant answers in community forums, but it is incremental as it combines existing neural network techniques.
The paper tackled answer selection in community question answering by framing it as an answer sequence labeling task, using CNNs and LSTMs to achieve effective results on the SemEval 2015 dataset.
In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of question-answer pair firstly, and then uses the joint representation as input of the long short-term memory (LSTM) to learn the answer sequence of a question for labeling the matching quality of each answer. Experiments conducted on the SemEval 2015 CQA dataset shows the effectiveness of our approach.