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Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation (1906.00363v2)
Published 2 Jun 2019 in cs.AI and cs.CL
Abstract: Introducing common sense to natural language understanding systems has received increasing research attention. It remains a fundamental question on how to evaluate whether a system has a sense making capability. Existing benchmarks measures commonsense knowledge indirectly and without explanation. In this paper, we release a benchmark to directly test whether a system can differentiate natural language statements that make sense from those that do not make sense. In addition, a system is asked to identify the most crucial reason why a statement does not make sense. We evaluate models trained over large-scale LLMing tasks as well as human performance, showing that there are different challenges for system sense making.
- Cunxiang Wang (30 papers)
- Shuailong Liang (6 papers)
- Yue Zhang (618 papers)
- Xiaonan Li (48 papers)
- Tian Gao (57 papers)