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.

基于梯度的解释方法在自然语言处理中被越来越多地用于解释神经模型，该论文提出了一种基于信息瓶颈的梯度解释框架用于方面感情分析，通过识别与情感相关的特征，显著提高了模型的性能和可解释性。