Aspect Sentiment Triplet Extraction (ASTE) is a fine-grained task in Aspect-Based Sentiment Analysis (ABSA), aiming to extract triplets of aspect terms, their corresponding opinion terms, and sentiment orientation. Recent studies have employed semantic and syntactic features to enhance extraction performance. However, as the extraction of both syntactic and semantic information is not task-specific, such information does not target aspect-opinion pairs within sentences, thereby potentially introducing noise unrelated to the task. To address this issue, we propose a Syntactic-Guided Semantic Attention Matrix Enhancement (SG-SAME) framework. First, we use a multi-view masking mechanism and a biaffine-based gating module to derive a task-aligned shared matrix from the existing semantic and syntactic matrices. Second, the shared matrix is injected into the attention score matrix of LLM layers to enhance the model’s representation learning. Finally, we propose a chain-of-thought (CoT) framework with negative candidate injection to steer the model toward autonomous reasoning and error self-rectification. Experimental results on multiple benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches in ASTE tasks. Code will be available.

Generative Error Correction (GEC) has emerged as a powerful post-processing method to boost the performance of Automatic Speech Recognition (ASR) systems. In this paper, we first show that GEC models struggle to generalize beyond the specific types of errors encountered during training, limiting their ability to correct new, unseen errors at test time, particularly in out-of-domain (OOD) scenarios. This phenomenon amplifies with named entities (NEs), where, in addition to insufficient contextual information or knowledge about the NEs, novel NEs keep emerging. To address these issues, we propose DARAG (Data- and Retrieval-Augmented Generative Error Correction), a novel approach designed to improve GEC for ASR in in-domain (ID) and OOD scenarios. First, we augment the GEC training dataset with synthetic data generated using foundational generative models, thereby simulating additional errors from which the model can learn from. For out-of-domain scenarios, we simulate test-time errors from new domains similarly and in an unsupervised fashion. Additionally, to better handle NEs, we introduce retrieval-augmented correction wherein we augment the model input with entities retrieved from a datastore of NEs. Our approach is simple, scalable, and both domain- and language-agnostic. We experiment on multiple datasets and settings, showing that DARAG outperforms all our baselines, achieving 8%–30% relative WER improvements in ID and 10%–33% improvements in OOD settings.