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.

翻译：基于梯度的解释方法因其高保真度而在自然语言处理中越来越多地用于解释神经模型。这类方法通常通过范数函数，利用维度层面的梯度值确定词级重要性，并假设所有梯度维度具有同等重要性。然而，在方面级情感分析（ABSA）中，我们的初步研究表明仅部分维度具有相关性。针对这一问题，我们提出了基于信息瓶颈的梯度（\texttt{IBG}）解释框架用于ABSA。该框架利用信息瓶颈将词嵌入精炼为简洁的内在维度，保留关键特征并剔除无关信息。综合实验表明，我们的\texttt{IBG}方法通过识别情感感知特征，显著提升了模型的性能和可解释性。