Comparative reviews are pivotal in understanding consumer preferences and influencing purchasing decisions. Comparative Quintuple Extraction (COQE) aims to identify five key components in text: the target entity, compared entities, compared aspects, opinions on these aspects, and polarity. Extracting precise comparative information from product reviews is challenging due to nuanced language and sequential task errors in traditional methods. To mitigate these problems, we propose MTP-COQE, an end-to-end model designed for COQE. Leveraging multi-perspective prompt-based learning, MTP-COQE effectively guides the generative model in comparative opinion mining tasks. Evaluation on the Camera-COQE (English) and VCOM (Vietnamese) datasets demonstrates MTP-COQE's efficacy in automating COQE, achieving superior performance with a 1.41% higher F1 score than the previous baseline models on the English dataset. Additionally, we designed a strategy to limit the generative model's creativity to ensure the output meets expectations. We also performed data augmentation to address data imbalance and to prevent the model from becoming biased towards dominant samples.

翻译：比较性评论对于理解消费者偏好和影响购买决策至关重要。比较性五元组抽取旨在识别文本中的五个关键组成部分：目标实体、对比实体、对比方面、对这些方面的观点以及情感极性。由于产品评论中语言的微妙性以及传统方法中序列任务错误的存在，从中提取精确的比较信息具有挑战性。为了缓解这些问题，我们提出了MTP-COQE，一个专为COQE设计的端到端模型。通过利用多视角提示学习，MTP-COQE有效地引导生成模型完成比较性观点挖掘任务。在Camera-COQE（英文）和VCOM（越南文）数据集上的评估证明了MTP-COQE在自动化COQE方面的有效性，其在英文数据集上的F1分数比先前基线模型高出1.41%，表现出更优的性能。此外，我们设计了一种策略来限制生成模型的创造性，以确保输出符合预期。我们还进行了数据增强以解决数据不平衡问题，并防止模型偏向于占主导地位的样本。