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Generate Then Correct: Single Shot Global Correction for Aspect Sentiment Quad Prediction

arXiv:2603.13777v1 Announce Type: new Abstract: Aspect-based sentiment analysis (ABSA) extracts aspect-level sentiment signals from user-generated text, supports product analytics, experience monitoring, and public-opinion tracking, and is central to fine-grained opinion mining. A key challenge in ABSA is aspect sentiment quad prediction (ASQP), which requires identifying four elements: the aspect term, the aspect category, the opinion term, and the sentiment polarity. However, existing studies usually linearize the unordered quad set into a fixed-order template and decode it left-to-right. With teacher forcing training, the resulting training-inference mismatch (exposure bias) lets early prefix errors propagate to later elements. The linearization order determines which elements appear earlier in the prefix, so this propagation becomes order-sensitive and is hard to repair in a single pass. To address this, we propose a method, Generate-then-Correct (G2C): a generator drafts quads an

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Shidong He, Haoyu Wang, Wenjie Luo · · 1 min read · 34 views
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arXiv:2603.13777v1 Announce Type: new Abstract: Aspect-based sentiment analysis (ABSA) extracts aspect-level sentiment signals from user-generated text, supports product analytics, experience monitoring, and public-opinion tracking, and is central to fine-grained opinion mining. A key challenge in ABSA is aspect sentiment quad prediction (ASQP), which requires identifying four elements: the aspect term, the aspect category, the opinion term, and the sentiment polarity. However, existing studies usually linearize the unordered quad set into a fixed-order template and decode it left-to-right. With teacher forcing training, the resulting training-inference mismatch (exposure bias) lets early prefix errors propagate to later elements. The linearization order determines which elements appear earlier in the prefix, so this propagation becomes order-sensitive and is hard to repair in a single pass. To address this, we propose a method, Generate-then-Correct (G2C): a generator drafts quads and a corrector performs a single-shot, sequence-level global correction trained on LLM-synthesized drafts with common error patterns. On the Rest15 and Rest16 datasets, G2C outperforms strong baseline models.

Executive Summary

This study proposes a novel approach to aspect sentiment quad prediction (ASQP), a critical task in aspect-based sentiment analysis (ABSA). The proposed method, Generate-then-Correct (G2C), addresses the training-inference mismatch issue in existing approaches by introducing a generator to draft quads and a corrector to perform a single-shot, sequence-level global correction. G2C outperforms strong baseline models on the Rest15 and Rest16 datasets, demonstrating its effectiveness in ASQP. The study contributes to the development of more accurate and efficient ABSA systems, which have significant applications in product analytics, experience monitoring, and public-opinion tracking.

Key Points

  • ▸ G2C addresses the training-inference mismatch issue in ASQP
  • ▸ The method consists of a generator and a corrector for single-shot, sequence-level global correction
  • ▸ G2C outperforms strong baseline models on the Rest15 and Rest16 datasets

Merits

Strength in Addressing Training-Inference Mismatch

G2C effectively addresses the training-inference mismatch issue in ASQP, which is a significant limitation in existing approaches. By introducing a corrector to perform sequence-level global correction, G2C improves the accuracy and efficiency of ASQP.

Improved Performance on Standard Datasets

G2C achieves state-of-the-art performance on the Rest15 and Rest16 datasets, demonstrating its effectiveness in ASQP. This improvement is a significant contribution to the development of more accurate and efficient ABSA systems.

Demerits

Limited Evaluation on Real-World Applications

While G2C outperforms strong baseline models on standard datasets, its performance on real-world applications is not evaluated. Further research is needed to assess G2C's effectiveness in practical settings.

Dependence on LLM-Synthesized Drafts

G2C relies on LLM-synthesized drafts to train the corrector, which may not generalize well to other datasets or applications. Further research is needed to explore alternative training methods.

Expert Commentary

The study proposes a novel approach to ASQP, which addresses a significant limitation in existing approaches. G2C's improved performance on standard datasets is a significant contribution to the development of more accurate and efficient ABSA systems. However, further research is needed to evaluate G2C's performance on real-world applications and to explore alternative training methods. The study's findings have significant implications for the development of ABSA systems and their applications in product analytics, experience monitoring, and public-opinion tracking.

Recommendations

  • ✓ Further research is needed to evaluate G2C's performance on real-world applications and to explore alternative training methods.
  • ✓ The study's findings should be replicated and validated on larger and more diverse datasets to assess its generalizability and robustness.

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