A Multi-View Fusion Neural Network for Answer Selection


  • Lei Sha Peking University
  • Xiaodong Zhang Peking University
  • Feng Qian Peking University
  • Baobao Chang Peking University
  • Zhifang Sui Peking University


deep learning, answer selection, multi-view


Community question answering aims at choosing the most appropriate answer for a given question, which is important in many NLP applications. Previous neural network-based methods consider several different aspects of information through calculating attentions. These different kinds of attentions are always simply summed up and can be seen as a ``single view", causing severe information loss. To overcome this problem, we propose a Multi-View Fusion Neural Network, where each attention component generates a ``view'' of the QA pair and a fusion RNN integrates the generated views to form a more holistic representation.    In this fusion RNN method, a filter gate  collects  important information of  input and directly adds it to the output, which borrows the idea of residual networks.    Experimental results on the WikiQA and SemEval-2016 CQA datasets demonstrate that our proposed model outperforms the state-of-the-art methods.




How to Cite

Sha, L., Zhang, X., Qian, F., Chang, B., & Sui, Z. (2018). A Multi-View Fusion Neural Network for Answer Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11989