Lin C.-AHung S.-SHuang H.-HHSIN-HSI CHEN2021-09-022021-09-022020https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096621390&partnerID=40&md5=6a63bb13516fa3cf21ae00a1be2e5a2ahttps://scholars.lib.ntu.edu.tw/handle/123456789/581360Chinese discourse parsing, which aims to identify the hierarchical relationships of Chinese elementary discourse units, has not yet a consistent evaluation metric. Although Parseval is commonly used, variations of evaluation differ from three aspects: micro vs. macro F1 scores, binary vs. multiway ground truth, and left-heavy vs. right-heavy binarization. In this paper, we first propose a neural network model that unifies a pre-trained transformer and CKY-like algorithm, and then compare it with the previous models with different evaluation scenarios. The experimental results show that our model outperforms the previous systems. We conclude that (1) the pre-trained context embedding provides effective solutions to deal with implicit semantics in Chinese texts, and (2) using multiway ground truth is helpful since different binarization approaches lead to significant differences in performance. ? European Language Resources Association (ELRA), licensed under CC-BY-NCBinarizations; Chinese text; Discourse parsing; Effective solution; Evaluation metrics; Ground truth; Implicit semantics; Neural network model; Semanticsconference paper2-s2.0-85096621390