@inproceedings{wagner-foster-2023-investigating,
title = "Investigating the Saliency of Sentiment Expressions in Aspect-Based Sentiment Analysis",
author = "Wagner, Joachim and
Foster, Jennifer",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.807",
doi = "10.18653/v1/2023.findings-acl.807",
pages = "12751--12769",
abstract = "We examine the behaviour of an aspect-based sentiment classifier built by fine-tuning the BERT BASE model on the SemEval 2016 English dataset. In a set of masking experiments, we examine the extent to which the tokens identified as salient by LIME and a gradient-based method are being used by the classifier. We find that both methods are able to produce faithful rationales, with LIME outperforming the gradient-based method. We also identify a set of manually annotated sentiment expressions for this dataset, and carry out more masking experiments with these as human rationales. The enhanced performance of a classifier that only sees the relevant sentiment expressions suggests that they are not being used to their full potential. A comparison of the LIME and gradient rationales with the sentiment expressions reveals only a moderate level of agreement. Some disagreements are related to the fixed length of the rationales and the tendency of the rationales to contain content words related to the aspect itself.",
}
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%0 Conference Proceedings
%T Investigating the Saliency of Sentiment Expressions in Aspect-Based Sentiment Analysis
%A Wagner, Joachim
%A Foster, Jennifer
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F wagner-foster-2023-investigating
%X We examine the behaviour of an aspect-based sentiment classifier built by fine-tuning the BERT BASE model on the SemEval 2016 English dataset. In a set of masking experiments, we examine the extent to which the tokens identified as salient by LIME and a gradient-based method are being used by the classifier. We find that both methods are able to produce faithful rationales, with LIME outperforming the gradient-based method. We also identify a set of manually annotated sentiment expressions for this dataset, and carry out more masking experiments with these as human rationales. The enhanced performance of a classifier that only sees the relevant sentiment expressions suggests that they are not being used to their full potential. A comparison of the LIME and gradient rationales with the sentiment expressions reveals only a moderate level of agreement. Some disagreements are related to the fixed length of the rationales and the tendency of the rationales to contain content words related to the aspect itself.
%R 10.18653/v1/2023.findings-acl.807
%U https://aclanthology.org/2023.findings-acl.807
%U https://doi.org/10.18653/v1/2023.findings-acl.807
%P 12751-12769
Markdown (Informal)
[Investigating the Saliency of Sentiment Expressions in Aspect-Based Sentiment Analysis](https://aclanthology.org/2023.findings-acl.807) (Wagner & Foster, Findings 2023)
ACL