On the Diversity and Limits of Human Explanations

Chenhao Tan


Abstract
A growing effort in NLP aims to build datasets of human explanations. However, it remains unclear whether these datasets serve their intended goals. This problem is exacerbated by the fact that the term explanation is overloaded and refers to a broad range of notions with different properties and ramifications. Our goal is to provide an overview of the diversity of explanations, discuss human limitations in providing explanations, and ultimately provide implications for collecting and using human explanations in NLP.Inspired by prior work in psychology and cognitive sciences, we group existing human explanations in NLP into three categories: proximal mechanism, evidence, and procedure. These three types differ in nature and have implications for the resultant explanations. For instance, procedure is not considered explanation in psychology and connects with a rich body of work on learning from instructions. The diversity of explanations is further evidenced by proxy questions that are needed for annotators to interpret and answer “why is [input] assigned [label]”. Finally, giving explanations may require different, often deeper, understandings than predictions, which casts doubt on whether humans can provide valid explanations in some tasks.
Anthology ID:
2022.naacl-main.158
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2173–2188
Language:
URL:
https://aclanthology.org/2022.naacl-main.158
DOI:
10.18653/v1/2022.naacl-main.158
Bibkey:
Cite (ACL):
Chenhao Tan. 2022. On the Diversity and Limits of Human Explanations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2173–2188, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
On the Diversity and Limits of Human Explanations (Tan, NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.158.pdf
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