Pinpointing Diffusion Grid Noise to Enhance Aspect Sentiment Quad Prediction (2024)

Abstract: Aspect sentiment quad prediction (ASQP) has garnered significant attention in aspect-based sentiment analysis (ABSA). Current ASQP research primarily relies on pre-trained generative language models to produce templated sequences, often complemented by grid-based auxiliary methods. Despite these efforts, the persistent challenge of generation instability remains unresolved and the effectiveness of grid methods remains underexplored in current studies. To this end, we introduce G rid Noise D iffusion P inpoint Network ( GDP ), a T5-based generative model aiming to tackle the issue of generation instability. The model consists of three novel modules, including Diffusion Vague Learning (DVL) to facilitate effective model learning and enhance overall robustness; Consistency Likelihood Learning (CLL) to discern the characteristics and commonalities of sentiment elements and thus reduce the impact of distributed noise; and GDP-FOR, a novel generation template, to enable models to generate outputs in a more natural way. Extensive experiments on four datasets demonstrate the remarkable effectiveness of our approach in addressing ASQP tasks.

## introduction aspect sentiment quad prediction (asqp) has recently attracted widespread attention in the field of aspect-based sentiment analysis (absa), a finegrained sentiment analysis task that aims to extract more comprehensive sentiment elements that include (1) aspect terms (at); (2) opinion terms (ot); asqp subtasks usually entail identifying at and excavating its corresponding ac, then establishing connections between them by incorporating supporting ot and/or sp. however, more than 30% of sentiment expressions manifest implicitly @xcite in practice, posing significant challenges in accurately extracting quadruples from sentences containing implicit at and/or ot. owing to its extensive applicability across various scenarios, considerable efforts have been devoted to absa @xcite . presently, there are two predominant methodologies. (1) pipeline approaches @xcite render multitasking learning more intuitive by dedicating each module to solving specific tasks. however, this method often overlooks inter-element relationships, rendering it susceptible to the cascading effects of error propagation @xcite . (2) generative approaches @xcite enable end-to-end solutions for asqp, thereby mitigating potential error propagation encountered in pipeline-based approaches. by learning to generate sentiment elements in the form of natural language, these approaches harness the semantics of sentiment elements to the fullest, specifically addressing the challenges associated with easily omitted at and ot elements. while the aforementioned methodologies offer valuable insights into addressing asqp tasks, they fall short in generating stable outputs, including errors in outputting semantically similar words, inclusion of unfriendly implicit words, and output duplication, as illustrated in the lower half of figure 1 . @xcite introduced a span-based method to address these issues by serializing sentimentrelated "word sequences" and rating them. however, this approach is prone to overlooking scattered or implicit sentiment information. @xcite proposed a table-filling approach, but this method diminishes the model's sensitivity to the interplay among elements. furthermore, @xcite argue that independently assigning each label in the table-filling method may lead to discrepancies, potentially resulting in incorrect output predictions. therefore, we take notice of the diffusion models @xcite that were employed to address noise in signal propagation. the training process of the diffusion model involves two stages: the forward noise addition process and the reverse denoising process. the model is based on a neural network, which has memory and denoising capabilities and can effectively learn the characteristics of the datasets. drawing inspiration from these models, we introduce grid noise diffusion pinpoint network (gdp), a t5-based generative model aiming to tackle the issue of generation instability. within our model, we propose the diffusion vague learning (dvl) mechanism to emulate the forward process to construct vague grids and utilize a golden grid to guide the backward convergence process. furthermore, we introduce a novel loss calculation target called consistency likelihood learning (cll) to consolidate identical sentiment elements and thereby reduce each elements distribution noise generated during training process. finally, we design a novel output template named gdp-for to enhance the naturalness of the generated results. the contributions of this paper are outlined as follows: ## asqp task definition we adhere to the definitions established in previous works focused on generation-based methods @xcite @xcite . the asqp task aims to predict all aspect-level quadruplets q 1 , q 2 , ..., q n in the source sentence s, where q @xmath0, ac i , ot i , sp i ). each component represents the aspect term, aspect category, opinion term, and sentiment polarity. quadruples will be marked as implicit when lacking clear at or ot (peper and wang, 2022). ## methodology in this section, we will introduce the constituent elements of gdp: (1) diffusion vague learning (dvl), a noise mitigation module; (2) consistency likelihood learning (cll), a novel objective for calculating sentiment distribution that aids in reducing noise generated by various sentiment elements; ## experimental setups in this section we introduce the detailed experimental setups on which our experiments rely. ## conclusion in this article, we note that previous studies on asqp have exclusively focused on essential content for model generation, overlooking the incorporation of various noise during the training process. in this research, we introduce a novel denoising approach called diffusion vague learning (dvl) and seamlessly integrate it into the t5 model, thus establishing a gdp model. simultaneously, we incorporate consistency likelihood learning (cll) to address the problem of sentiment element fragmentation during model training, effectively reducing the generation of noise. finally, we meticulously design an output template for the gdp model that aligns more closely with human intuition and grammatical rules. extensive experiments demonstrate that the gdp method achieves cutting-edge results.