Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce

Yincen Qu, Ningyu Zhang, Hui Chen, Zelin Dai, Chengming Wang, Xiaoyu Wang, Qiang Chen, Huajun Chen


Abstract
In e-commerce, the salience of commonsense knowledge (CSK) is beneficial for widespread applications such as product search and recommendation. For example, when users search for “running” in e-commerce, they would like to find products highly related to running, such as “running shoes” rather than “shoes”. Nevertheless, many existing CSK collections rank statements solely by confidence scores, and there is no information about which ones are salient from a human perspective. In this work, we define the task of supervised salience evaluation, where given a CSK triple, the model is required to learn whether the triple is salient or not. In addition to formulating the new task, we also release a new Benchmark dataset of Salience Evaluation in E-commerce (BSEE) and hope to promote related research on commonsense knowledge salience evaluation. We conduct experiments in the dataset with several representative baseline models. The experimental results show that salience evaluation is a hard task where models perform poorly on our evaluation set. We further propose a simple but effective approach, PMI-tuning, which shows promise for solving this novel problem. Code is available in https://github.com/OpenBGBenchmark/OpenBG-CSK.
Anthology ID:
2022.findings-emnlp.2
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
14–27
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.2
DOI:
10.18653/v1/2022.findings-emnlp.2
Bibkey:
Cite (ACL):
Yincen Qu, Ningyu Zhang, Hui Chen, Zelin Dai, Chengming Wang, Xiaoyu Wang, Qiang Chen, and Huajun Chen. 2022. Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 14–27, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Commonsense Knowledge Salience Evaluation with a Benchmark Dataset in E-commerce (Qu et al., Findings 2022)
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