%0 Journal Article %A YIN Yajue %A GAO Xiaoya %A WANG Jingjing %A LI Shoushan %A XU Shaoyang %A ZENG Yuhao %T Patent Matching with Multi-View Attentive Network %D 2022 %R %J Journal of Chinese Information Processing %P 106-113 %V 36 %N 7 %X The task of patent matching aims to determine the similarity between two patent texts. Different from the free texts, the patent text includes a variety of text blocks, such as title, abstract, statement, etc. In order to make full use of these multi-text information, this paper proposes a Multi-View Attentive Network (MVAN) learning model based on attention mechanism, so as to capture matching information from different perspectives of the patent. First, the BERT model is employed to extract each single-view matching features (title, abstract or statement) of a patent pair. Then, the attention mechanism is adopted to integrate the above-mentioned features and obtain multi-view matching features. Finally, the multi-view learning mechanism is applied to jointly learn single and multi-view matching features. Experimental results show that the performance of the proposed MVAN is better than other baseline methods on patent matching tasks. %U http://jcip.cipsc.org.cn/EN/abstract/article_3359.shtml