Work

Shallow Convolutional Neural Network for Implicit Discourse Relation Recognition

Year: 2015

Type: conference-paper

Abstract: Implicit discourse relation recognition remains a serious challenge due to the absence of discourse connectives.In this paper, we propose a Shallow Convolutional Neural Network (SCNN) for implicit discourse relation recognition, which contains only one hidden layer but is effective in relation recognition.The shallow structure alleviates the overfitting problem, while the convolution and nonlinear operations help preserve the recognition and generalization ability of our model.Experiments on the benchmark data set show that our model achieves comparable and even better performance when comparing against current state-of-the-art systems.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d15-1266

Authors: Biao Zhang, Jinsong Su, Deyi Xiong, Yaojie Lu, Hong Duan, Junfeng Yao

Institutions: Xiamen University, Soochow University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: National Natural Science Foundation of China, Government of Jiangsu Province, Natural Science Foundation of Fujian Province, Natural Science Foundation of Jiangsu Province

Awards: G2696153056, G7186630020, G8538348761