[2603.13777] Generate Then Correct: Single Shot Global Correction for Aspect Sentiment Quad Prediction

Computer Science > Computation and Language arXiv:2603.13777 (cs) [Submitted on 14 Mar 2026 (v1), last revised 4 Apr 2026 (this version, v2)] Title:Generate Then Correct: Single Shot Global Correction for Aspect Sentiment Quad Prediction Authors:Shidong He, Haoyu Wang, Wenjie Luo View a PDF of the paper titled Generate Then Correct: Single Shot Global Correction for Aspect Sentiment Quad Prediction, by Shidong He and 2 other authors View PDF HTML (experimental) 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 ...