@InProceedings{lin-sun-han:2017:EMNLP2017,
  author    = {Lin, Hongyu  and  Sun, Le  and  Han, Xianpei},
  title     = {Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2032--2043},
  abstract  = {Reasoning with commonsense knowledge is critical for natural language
	understanding. Traditional methods for commonsense machine comprehension mostly
	only focus on one specific kind of knowledge, neglecting the fact that
	commonsense reasoning requires simultaneously considering different kinds of
	commonsense knowledge. In this paper, we propose a multi-knowledge reasoning
	method, which can exploit heterogeneous knowledge for commonsense machine
	comprehension. Specifically, we first mine different kinds of knowledge
	(including event narrative knowledge, entity semantic knowledge and sentiment
	coherent knowledge) and encode them as inference rules with costs. Then we
	propose a multi-knowledge reasoning model, which selects inference rules for a
	specific reasoning context using attention mechanism, and reasons by
	summarizing all valid inference rules. Experiments on RocStories show that our
	method outperforms traditional models significantly.},
  url       = {https://www.aclweb.org/anthology/D17-1216}
}