Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.18145 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914693846663168 |
|---|---|
| author | Cheng, Zhenxiao Zhou, Jie Wu, Wen Chen, Qin He, Liang |
| author_facet | Cheng, Zhenxiao Zhou, Jie Wu, Wen Chen, Qin He, Liang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18145 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis Cheng, Zhenxiao Zhou, Jie Wu, Wen Chen, Qin He, Liang Computation and Language 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. |
| title | Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2402.18145 |