``Going on a vacation'' takes longer than ``Going for a walk'': A Study of Temporal Commonsense Understanding
Authors
Zhou, Ben and Khashabi, Daniel and Ning, Qiang and Roth, Dan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
2019
3363--3369
Association for Computational Linguistics
Abstract
Understanding time is crucial for understanding events expressed in natural language. Because people rarely say the obvious, it is often necessary to have commonsense knowledge about various temporal aspects of events, such as duration, frequency, and temporal order. However, this important problem has so far received limited attention. This paper systematically studies this temporal commonsense problem. Specifically, we define five classes of temporal commonsense, and use crowdsourcing to develop a new dataset, MCTACO, that serves as a test set for this task. We find that the best current methods used on MCTACO are still far behind human performance, by about 20{%}, and discuss several directions for improvement. We hope that the new dataset and our study here can foster more future research on this topic.
BibTeX Citation
@inproceedings{zhou-etal-2019-going,
title = "``Going on a vacation'' takes longer than ``Going for a walk'': A Study of Temporal Commonsense Understanding",
author = "Zhou, Ben and
Khashabi, Daniel and
Ning, Qiang and
Roth, Dan",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1332/",
doi = "10.18653/v1/D19-1332",
pages = "3363--3369",
abstract = "Understanding time is crucial for understanding events expressed in natural language. Because people rarely say the obvious, it is often necessary to have commonsense knowledge about various temporal aspects of events, such as duration, frequency, and temporal order. However, this important problem has so far received limited attention. This paper systematically studies this temporal commonsense problem. Specifically, we define five classes of temporal commonsense, and use crowdsourcing to develop a new dataset, MCTACO, that serves as a test set for this task. We find that the best current methods used on MCTACO are still far behind human performance, by about 20{\%}, and discuss several directions for improvement. We hope that the new dataset and our study here can foster more future research on this topic."
}