https://github.com/wangcunxiang/Sen-Making-and-Explanation

@inproceedings{wang-etal-2019-make,
   title = "Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation",
   author = "Wang, Cunxiang  and
     Liang, Shuailong  and
     Zhang, Yue  and
     Li, Xiaonan  and
     Gao, Tian",
   booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
   month = jul,
   year = "2019",
   address = "Florence, Italy",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/P19-1393",
   pages = "4020--4026",
   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 the sense-making capability. Existing benchmarks measure common sense knowledge indirectly or without reasoning. 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.",
}

https://github.com/wangcunxiang/Sen-Making-and-Explanation

@inproceedings{wang-etal-2019-make,
   title = "Does it Make Sense? And Why? A Pilot Study for Sense Making and Explanation",
   author = "Wang, Cunxiang  and
     Liang, Shuailong  and
     Zhang, Yue  and
     Li, Xiaonan  and
     Gao, Tian",
   booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
   month = jul,
   year = "2019",
   address = "Florence, Italy",
   publisher = "Association for Computational Linguistics",
   url = "https://www.aclweb.org/anthology/P19-1393",
   pages = "4020--4026",
   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 the sense-making capability. Existing benchmarks measure common sense knowledge indirectly or without reasoning. 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.",
}