Incorporating label dependency for answer quality tagging in community question answering via CNN-LSTM-CRF

In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers. In this paper, we show that the dependency between the answer quality labels also plays a pivotal role. To validate the effectiveness of label dependency, we propose two neural network-based models, with different combination modes of Convolutional Neural Networks, Long Short Term Memory and Conditional Random Fields. Extensive experiments are taken on the dataset released by the SemEval-2015 cQA shared task. The first model is a stacked ensemble of the networks. It achieves 58.96% on macro averaged F₁, which improves the state-of-the-art neural network-based method by 2.82% and outperforms the Top-1 system in the shared task by 1.77%. The second is a simple attention-based model whose input is the connection of the question and its corresponding answers. It produces promising results with 58.29% on overall F₁ and gains the best performance on the Good and Bad categories.