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 language modeling tasks as well as human performance, showing that there are different challenges for system sense making.
@article{arxiv.1906.00363,
title = {Does It Make Sense? And Why? A Pilot Study for Sense Making and Explanation},
author = {Cunxiang Wang and Shuailong Liang and Yue Zhang and Xiaonan Li and Tian Gao},
journal= {arXiv preprint arXiv:1906.00363},
year = {2020}
}