@InProceedings{xu-EtAl:2018:C18-12,
  author    = {Xu, Sheng  and  Li, Peifeng  and  Zhou, Guodong  and  Zhu, Qiaoming},
  title     = {Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {525--535},
  abstract  = {The task of nuclearity recognition in Chinese discourse remains challenging due to the demand for more deep semantic information. In this paper, we propose a novel text matching network (TMN) that encodes the discourse units and the paragraphs by combining Bi-LSTM and CNN to capture both global dependency information and local n-gram information. Moreover, it introduces three components of text matching, the Cosine, Bilinear and Single Layer Network, to incorporate various similarities and interactions among the discourse units. Experimental results on the Chinese Discourse TreeBank show that our proposed TMN model significantly outperforms various strong baselines in both micro-F1 and macro-F1.},
  url       = {http://www.aclweb.org/anthology/C18-1044}
}