| | --- |
| | task_categories: |
| | - text-classification |
| | - multiple-choice |
| | language: |
| | - en |
| | tags: |
| | - explanation |
| | --- |
| | 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.", |
| | } |
| | ``` |
