Work

Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification

Year: 2017

Type: conference-paper

Abstract: We present a novel multi-task attentionbased neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attentionbased neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora.The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets).Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.

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

DOI: 10.18653/v1/d17-1134

Authors: Man Lan, Jianxiang Wang, Yuanbin Wu, Zheng-Yu Niu, Haifeng Wang

Institutions: East China Normal University, Baidu (China)

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, Science and Technology Commission of Shanghai Municipality

Awards: G2061749428, G3200469410, G3243303943, G5661849975