%A Wang Hong, Shu Zhan, Gao Yinquan, Tian Wenhong %T Analyzing Implicit Discourse Relation with Single Classifier and Multi-Task Network %0 Journal Article %D 2021 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2021.0347 %P 80-88 %V 5 %N 11 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_5125.shtml} %8 2021-11-25 %X

[Objective] This paper proposes a new method to identify implicit discourse relations based on a single classifier and multi-task learning model. [Methods] First,we modeled the implicit and explicit discourse relationships with the multi-task learning method. Then, we converted the four classification problems to two and trained the single classifier. [Results] We examined our new method with the HIT-CDTB data set. For the corpus with extended and parallel relations, the F1 values reached 0.94 and 0.81 respectively, which were significantly improved with four inter-sentence relations. [Limitations] The performance of our model could be improved with more distributed and expanded datasets. [Conclusions] The proposed method yields the best results with the HIT-CDTB data set. Deleting connectives will add noise to the training set and negatively affect the model’s performance.