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 multiknowledge 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. Premise Document Wrong Hypothesis Ron started his new job as a landscaper today. He loves the outdoors and has always enjoyed working in it. His boss tells him to re-sod the front yard of the mayor's home. Ron is ecstatic, but does a thorough job and finishes super early. His boss commends him for a job well done. Ron is immediately fired for insubordination. One day, my sister came over to the house to show us her puppy. She told us that she had just gotten the puppy across the street. My sons begged me to get them one. I told them that if they would care for it, they could have it. My son said they would, so we got a dog. We then grabbed a small kitten.
REF propose a multiknowledge reasoning approach which could exploit heterogeneous knowledge.
Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension
EMNLP
Computer Science