|
--- |
|
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.", |
|
} |
|
``` |
