DiffuSent: Towards a Unified Diffusion Framework for Aspect-Based Sentiment Analysis

• URL: http://arxiv.org/abs/2606.01323v1
• Date: Sun, 31 May 2026 16:15:03 GMT
• Title: DiffuSent: Towards a Unified Diffusion Framework for Aspect-Based Sentiment Analysis
• Abstract summary: We present DiffuSent, a non-auto-regressive diffusion framework that formulates all ABSA subtasks as boundary denoising diffusion processes.<n>We show that DiffuSent exhibits notable gains on multi-word triplets, achieving an average improvement of +2.48 F1, and maintains robust extraction accuracy in sentences containing multiple sentiment triplets.
• Score: 4.022262570915779
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Aspect-Based Sentiment Analysis (ABSA) encompasses seven distinct subtasks, each focusing on different extracted elements. Despite the proven success of generative models in unified aspect sentiment analysis, existing approaches often rely on auto-regressive token-by-token generation without grasping the whole information of the aspect and opinion terms, resulting in boundary insensitivity, particularly in context of multi-word aspect and opinion terms. To address these issues, we present DiffuSent, a non-auto-regressive diffusion framework that systematically formulates all ABSA subtasks as boundary denoising diffusion processes, progressively refining boundaries over noisy states. Furthermore, we introduce a contrastive denoising training strategy which effectively address duplicate predictions with subtle variations introduced by diffusion process. Extensive experiments across 28 settings (7 subtasks x 4 datasets) demonstrate that DiffuSent achieves delivers consistent improvements over the strongest generative and span-based systems. DiffuSent exhibits notable gains on multi-word triplets, achieving an average improvement of +2.48 F1, and maintains robust extraction accuracy in sentences containing multiple sentiment triplets. Moreover, the non-auto-regressive decoding enables substantial efficiency benefits, reaching up to 181 times faster inference than auto-regressive generative baselines

Related papers

• More Perspectives, Stronger Signals: Multi-Perspective Enhancement and Progressive Fusion for Multimodal Entity Representation Learning [6.4639268448773946]
  We propose PrismF, a unified framework that synergizes multi-perspective enhancement with progressive fusion to extract stronger signals from diverse inputs.<n>PrismF enhances fine-grained intra-modal semantics through a multi-perspective mechanism that decomposes each modality into complementary views.<n>Experiments show that PrismF achieves the strongest overall performance, including relative improvements of 4.04% in MRR and 11.17% in Hits@1 on KVC16K.
  arXiv  Detail & Related papers  (2026-08-29T08:29:43Z)
• WAM-Diff2: Hierarchical AR-to-Diffusion Distillation for Highly Efficient Autonomous Driving VLA [27.102605117827398]
  Vision-Language-Action (VLA) models have emerged as a prominent paradigm for end-to-end autonomous driving.<n>They are constrained by high computational latency and exposure bias arising from sequential autoregressive decoding.<n>We introduce WAM-Diff2, a multi-task discrete diffusion VLA framework powered by a three-stage hierarchical strategy.
  arXiv  Detail & Related papers  (2026-08-02T06:45:16Z)
• Semantic Trimming and Auxiliary Multi-step Prediction for Generative Recommendation [22.025559532445012]
  We propose STAMP (Semantic Trimming and Auxiliary Multi-step Prediction), a framework utilizing a dual-end optimization strategy.<n>Experiments on public Amazon and large-scale industrial datasets show STAMP achieves 1.23--1.38$times$ speedup and 17.2%--54.7% VRAM reduction.
  arXiv  Detail & Related papers  (2026-04-07T02:00:23Z)
• Hit-RAG: Learning to Reason with Long Contexts via Preference Alignment [97.55382322103986]
  Hit-RAG is a multi-stage preference alignment framework designed to resolve cognitive bottlenecks.<n>Our approach systematically refines the utilization of external evidence via three distinct stages.
  arXiv  Detail & Related papers  (2026-03-07T04:05:27Z)
• Diverse Text-to-Image Generation via Contrastive Noise Optimization [60.48914865049489]
  Text-to-image (T2I) diffusion models have demonstrated impressive performance in generating high-fidelity images.<n>Existing approaches typically optimize intermediate latents or text conditions during inference.<n>We introduce Contrastive Noise Optimization, a simple yet effective method that addresses the diversity issue from a distinct perspective.
  arXiv  Detail & Related papers  (2025-10-04T13:51:32Z)
• Explaining multimodal LLMs via intra-modal token interactions [55.27436637894534]
  Multimodal Large Language Models (MLLMs) have achieved remarkable success across diverse vision-language tasks, yet their internal decision-making mechanisms remain insufficiently understood.<n>We propose enhancing interpretability by leveraging intra-modal interaction.
  arXiv  Detail & Related papers  (2025-09-26T14:39:13Z)
• Beyond Quantity: Distribution-Aware Labeling for Visual Grounding [72.43984105242177]
  Visual grounding requires large and diverse region-text pairs.<n>Existing pseudo-labeling pipelines often overfit to biased distributions.<n>We propose DAL, a distribution-aware labeling framework for visual grounding.
  arXiv  Detail & Related papers  (2025-05-30T09:04:47Z)
• An Effective Deployment of Diffusion LM for Data Augmentation in Low-Resource Sentiment Classification [2.0930389307057427]
  Sentiment classification (SC) often suffers from low-resource challenges such as domain-specific contexts, imbalanced label distributions, and few-shot scenarios. We propose Diffusion LM to capture in-domain knowledge and generate pseudo samples by reconstructing strong label-related tokens.
  arXiv  Detail & Related papers  (2024-09-05T02:51:28Z)
• Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce Classification [6.442738337380714]
  We argue that diffusion-based DA methods cannot take account of both faithfulness and diversity, which are two critical keys for generating high-quality samples. We propose a novel Diffusion-based DA method: Diff-II.
  arXiv  Detail & Related papers  (2024-08-29T05:05:02Z)
• Improving the Robustness of Summarization Systems with Dual Augmentation [68.53139002203118]
  A robust summarization system should be able to capture the gist of the document, regardless of the specific word choices or noise in the input. We first explore the summarization models' robustness against perturbations including word-level synonym substitution and noise. We propose a SummAttacker, which is an efficient approach to generating adversarial samples based on language models.
  arXiv  Detail & Related papers  (2023-06-01T19:04:17Z)
• Dense Contrastive Visual-Linguistic Pretraining [53.61233531733243]
  Several multimodal representation learning approaches have been proposed that jointly represent image and text. These approaches achieve superior performance by capturing high-level semantic information from large-scale multimodal pretraining. We propose unbiased Dense Contrastive Visual-Linguistic Pretraining to replace the region regression and classification with cross-modality region contrastive learning.
  arXiv  Detail & Related papers  (2021-09-24T07:20:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.