@inproceedings{cheng-etal-2024-learning-intrinsic,
title = "Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis",
author = "Cheng, Zhenxiao and
Zhou, Jie and
Wu, Wen and
Chen, Qin and
He, Liang",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.897",
pages = "10274--10285",
abstract = "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 (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 IBG approach considerably improves both the models{'} performance and the explanations{'} clarity by identifying sentiment-aware features.",
}
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<abstract>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 (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 IBG approach considerably improves both the models’ performance and the explanations’ clarity by identifying sentiment-aware features.</abstract>
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%0 Conference Proceedings
%T Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis
%A Cheng, Zhenxiao
%A Zhou, Jie
%A Wu, Wen
%A Chen, Qin
%A He, Liang
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F cheng-etal-2024-learning-intrinsic
%X 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 (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 IBG approach considerably improves both the models’ performance and the explanations’ clarity by identifying sentiment-aware features.
%U https://aclanthology.org/2024.lrec-main.897
%P 10274-10285
Markdown (Informal)
[Learning Intrinsic Dimension via Information Bottleneck for Explainable Aspect-based Sentiment Analysis](https://aclanthology.org/2024.lrec-main.897) (Cheng et al., LREC-COLING 2024)
ACL