Enhancing Aspect Sentiment Quad Prediction through Dual-Sequence Data Augmentation and Contrastive Learning

Shihao Li, Nankai Lin, Pinmo Wu, Dong Zhou, Aimin Yang
Proceedings of the 16th Asian Conference on Machine Learning, PMLR 260:447-462, 2025.

Abstract

Aspect sentiment quad prediction (ASQP) endeavors to analyze four sentiment elements in sentences. Recent studies utilize generative models to achieve this task, yielding commendable outcomes. However, these studies often fall short of fully leveraging the relationships between sentiment elements and have difficulty effectively handling implicit sentiment expressions. Furthermore, this task also confronts the obstacle of data scarcity stemming from the substantial expenses involved in data annotation. To address these limitations, we propose dual-sequence data augmentation to achieve diverse input and target expressions, while we incorporate contrastive learning to instigate the model to distinctly represent the presence or absence of these pivotal features pertaining to implicit aspects and opinion terms. Additionally, we introduce a prediction normalization strategy to refine the produced results. Empirical findings from experiments on four publicly available datasets show the superiority of our method, surpassing multiple baseline approaches and achieving state-of-the-art performance on the benchmark.

Cite this Paper

BibTeX
@InProceedings{pmlr-v260-li25b, title = {Enhancing Aspect Sentiment Quad Prediction through Dual-Sequence Data Augmentation and Contrastive Learning}, author = {Li, Shihao and Lin, Nankai and Wu, Pinmo and Zhou, Dong and Yang, Aimin}, booktitle = {Proceedings of the 16th Asian Conference on Machine Learning}, pages = {447--462}, year = {2025}, editor = {Nguyen, Vu and Lin, Hsuan-Tien}, volume = {260}, series = {Proceedings of Machine Learning Research}, month = {05--08 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v260/main/assets/li25b/li25b.pdf}, url = {https://proceedings.mlr.press/v260/li25b.html}, abstract = {Aspect sentiment quad prediction (ASQP) endeavors to analyze four sentiment elements in sentences. Recent studies utilize generative models to achieve this task, yielding commendable outcomes. However, these studies often fall short of fully leveraging the relationships between sentiment elements and have difficulty effectively handling implicit sentiment expressions. Furthermore, this task also confronts the obstacle of data scarcity stemming from the substantial expenses involved in data annotation. To address these limitations, we propose dual-sequence data augmentation to achieve diverse input and target expressions, while we incorporate contrastive learning to instigate the model to distinctly represent the presence or absence of these pivotal features pertaining to implicit aspects and opinion terms. Additionally, we introduce a prediction normalization strategy to refine the produced results. Empirical findings from experiments on four publicly available datasets show the superiority of our method, surpassing multiple baseline approaches and achieving state-of-the-art performance on the benchmark.} }
Endnote
%0 Conference Paper %T Enhancing Aspect Sentiment Quad Prediction through Dual-Sequence Data Augmentation and Contrastive Learning %A Shihao Li %A Nankai Lin %A Pinmo Wu %A Dong Zhou %A Aimin Yang %B Proceedings of the 16th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Vu Nguyen %E Hsuan-Tien Lin %F pmlr-v260-li25b %I PMLR %P 447--462 %U https://proceedings.mlr.press/v260/li25b.html %V 260 %X Aspect sentiment quad prediction (ASQP) endeavors to analyze four sentiment elements in sentences. Recent studies utilize generative models to achieve this task, yielding commendable outcomes. However, these studies often fall short of fully leveraging the relationships between sentiment elements and have difficulty effectively handling implicit sentiment expressions. Furthermore, this task also confronts the obstacle of data scarcity stemming from the substantial expenses involved in data annotation. To address these limitations, we propose dual-sequence data augmentation to achieve diverse input and target expressions, while we incorporate contrastive learning to instigate the model to distinctly represent the presence or absence of these pivotal features pertaining to implicit aspects and opinion terms. Additionally, we introduce a prediction normalization strategy to refine the produced results. Empirical findings from experiments on four publicly available datasets show the superiority of our method, surpassing multiple baseline approaches and achieving state-of-the-art performance on the benchmark.
APA
Li, S., Lin, N., Wu, P., Zhou, D. & Yang, A.. (2025). Enhancing Aspect Sentiment Quad Prediction through Dual-Sequence Data Augmentation and Contrastive Learning. Proceedings of the 16th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 260:447-462 Available from https://proceedings.mlr.press/v260/li25b.html.

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