利用Attentive來改善端對端中文語篇剖析遞迴類神經網路系統

摘要

Discourse parser helps us understand the relationship and connection between sentences and sentences from different angles, but the tree structure data still need to rely on manual marking, which makes this technology unable to be directly used in daily life. So far, there have been many research and studies on how to automatically construct the complete tree structure on the computer. Since deep learning has progressed rapidly in recent years, the construction method for discourse parser has also changed from the traditional SVM, CRF method to the current recursive neural network. In the Chinese corpus tree library CDTB, the parsing analysis problem can be divided into four main problems, including elementary discourse unit (EDU) segmentation, tree structure construction, center labeling, and sense labeling. In this paper, we use many state-of-the-art deep learning techniques, such as attentive recursive neural networks, self-attentive, and BERT to improve the performance. In the end, we succeeded in increasing the accuracy by more than 10% of F1 in each task, reaching the best performance we know so far.