TY - JOUR AU - Sha, Lei AU - Zhang, Xiaodong AU - Qian, Feng AU - Chang, Baobao AU - Sui, Zhifang PY - 2018/04/27 Y2 - 2024/03/28 TI - A Multi-View Fusion Neural Network for Answer Selection JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 32 IS - 1 SE - Main Track: NLP and Machine Learning DO - 10.1609/aaai.v32i1.11989 UR - https://ojs.aaai.org/index.php/AAAI/article/view/11989 SP - AB - <p> Community question answering aims at choosing the most appropriate answer for a given question, which is important in many NLP applications.<span> </span>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. </p> ER -