A Two-Level Attentive Pooling Based Hybrid Network for Question Answer Matching Task

Attention-based deep learning network models have shown obvious advantages on the sentence representation in many NLP tasks. While in the answer selection domain, applying attention-based deep learning model to capture complex semantic relations between question and answer is an extremely challenging task. In this paper, instead of simply using max-pooling in the pooling layer, we propose the two-level attentive pooling model which can efficiently select several key and high semantic-related matching words in the question-answer pair to improve the accuracy of answer selection. Specially, our model is built on top of the hybrid network which includes GRU and CNN to encode the complex sentence representation. The experimental evaluation on two popular datasets shows that our model has the good effectiveness and achieves the state-of-art performance in the answer selection task.