Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
<|reference_start|>Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis: Gradient-based explanation methods are increasingly used to interpret neural models in natural language processing (NLP) due to their high fidelity. Such methods determine word-level importance using dimension-level gradient values through a norm function, often presuming equal significance for all gradient dimensions. However, in the context of Aspect-based Sentiment Analysis (ABSA), our preliminary research suggests that only specific dimensions are pertinent. To address this, we propose the Information Bottleneck-based Gradient (\texttt{IBG}) explanation framework for ABSA. This framework leverages an information bottleneck to refine word embeddings into a concise intrinsic dimension, maintaining essential features and omitting unrelated information. Comprehensive tests show that our \texttt{IBG} approach considerably improves both the models' performance and interpretability by identifying sentiment-aware features.<|reference_end|>
@article{cheng2024learning,
    title={Learning Intrinsic Dimension via Information Bottleneck for Explainable
  Aspect-based Sentiment Analysis},
    author={Zhenxiao Cheng, Jie Zhou, Wen Wu, Qin Chen, Liang He},
    journal={arXiv preprint arXiv:2402.18145},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18145},
    primaryClass={cs.CL}
}