Abstract:

Existing models still face multiple challenges in Aspect Sentiment Quad Prediction （ASQP） task. They have difficulty in dealing with implicit sentiment expressions （such as implicit aspects or opinions） that lack explicit lexical cues， making it difficult for models to accurately capture sentiment tendencies. A quad prediction is considered correct only when all predicted elements of this prediction exactly match the correct elements. However， models may generate easily confused synonyms or synonymous words， leading to completely incorrect quad predictions. Moreover， existing models focus on improving the probability of predicting correct words， ignoring the suppression of easily confused words. Additionally， the cross-entropy loss used by these models makes them overconfident about incorrect predictions， lacking uncertainty modeling and thus failing in actively suppressing high-risk errors. These problems limit the performance of existing models in Aspect-Based Sentiment Analysis （ABSA） tasks. To address these problems， a Supervised Contrastive generative sentiment analysis method with Uncertainty-Aware Unlikelihood Learning （SCUAUL） was proposed. Firstly， supervised contrastive learning was used to shorten the semantic space distance of similar samples （e.g.， same sentiment polarity） through contrastive loss， enhancing the model's ability to distinguish key features （e.g.， sentiment polarity， implicit aspects） of input data. Secondly， Monte Carlo Dropout （MC Dropout） was used to capture the model's inherent uncertainty and identify easily confused words. By marginalizing unlikely learning， the generation probability of easily confused words was dynamically suppressed while maintaining the probability of generating correct words， and a minimum entropy constraint was combined to balance generation diversity and accuracy. Average results of five experiments on the Rest15 and Rest16 datasets showed that， compared with the suboptimal model AugABSA （data Augmentation by text generation for ABSA） and the classic model PARAPHRASE， SCUAUL improved precision by 0.40， 3.98 and 0.38， 3.83 percentage points， the recall by 0.30， 2.87 and 0.48， 2.88 percentage points， and the F1 score by 0.35， 3.43 and 0.42， 3.37 percentage points， respectively， verifying the effectiveness of SCUAUL in ABSA tasks.

Key words: Aspect-Based Sentiment Analysis (ABSA), uncertainty perception, unlikelihood learning, minimum entropy, contrastive learning

摘要：

现有模型在方面情感四元组预测（ASQP）任务中仍面临着多重挑战：它们在处理隐式情感表达（如隐含的方面或观点）时存在困难，隐式情感表达缺乏明确的词汇线索，导致模型难以准确捕捉情感倾向；只有当四元组预测的所有预测元素都与正确元素完全匹配时，模型才被认为是准确的，而模型会生成易混淆近义词或同义词，导致四元组预测完全错误。而且现有模型致力于提高预测正确词语的概率，忽略了抑制易混淆词的概率；同时，现有模型采用的交叉熵损失使模型对错误预测过于自信，缺乏对不确定性的建模，难以主动抑制高风险错误。这些问题限制了现有模型在基于方面的情感分析（ABSA）任务上的表现。为了解决这些问题，提出一种基于不确定性感知非似然学习的监督对比生成式情感分析方法（SCUAUL）。首先，采用监督对比学习，通过对比损失拉近同类样本（如相同情感极性）的语义空间距离，增强模型对输入数据的关键特征（如情感极性、隐式方面等）的区分能力；其次，利用蒙特卡洛Dropout（MC Dropout）捕捉模型内在不确定性，发现易混淆词，通过边缘化非似然学习动态抑制易混淆词汇的生成概率，保持正确词汇的生成概率，并结合最小熵约束平衡生成多样性与准确性。在Rest15和Rest16数据集上进行5次实验的平均结果显示，相较于次优模型AugABSA（data Augmentation by text generation for ABSA）和经典模型PARAPHRASE，SCUAUL的精确率分别提升了0.40、3.98和0.38、3.83个百分点，召回率分别提升了0.30、2.87和0.48、2.88个百分点，F1 score分别提升了0.35、3.43和0.42、3.37个百分点，验证了SCUAUL在ABSA任务上的有效性。

关键词: 基于方面的情感分析, 不确定性感知, 非似然学习, 最小熵, 对比学习