论文标题
具有对比度学习和表现力结构的基于生成方面的情感分析
Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive Structure
论文作者
论文摘要
生成模型在基于方面的情感分析（ABSA）任务上表现出了令人印象深刻的结果，尤其是针对提取方面类别 - 开放式索引（ACOS）四倍体的新兴任务。但是，这些模型在隐式情感表达中遇到了困难，这些模型通常是在在线评论等有用内容中观察到的。在这项工作中，我们介绍了Gen-SCL-NAT，其中包括两种用于改进ACOS四倍提取的结构化生成的技术。首先，我们提出了Gen-SCL，这是一个受监督的对比学习目标，通过鼓励模型产生在关键输入属性中可以区分的输入表示，例如情感极性以及隐性意见和方面的存在，可以帮助四倍的预测。其次，我们介绍了一种新的结构化生成格式Gen-Nat，可以更好地适应自回归编码器模型以生成方式提取四倍体。实验结果表明，Gen-SCL-NAT在三个ACO数据集中达到了最高的性能，平均F1提高了1.48％，而笔记本电脑L1数据集则最大增加了1.73％。此外，我们看到对现有ACO方法挑战的隐性方面和意见分裂的巨大收益。
Generative models have demonstrated impressive results on Aspect-based Sentiment Analysis (ABSA) tasks, particularly for the emerging task of extracting Aspect-Category-Opinion-Sentiment (ACOS) quadruples. However, these models struggle with implicit sentiment expressions, which are commonly observed in opinionated content such as online reviews. In this work, we introduce GEN-SCL-NAT, which consists of two techniques for improved structured generation for ACOS quadruple extraction. First, we propose GEN-SCL, a supervised contrastive learning objective that aids quadruple prediction by encouraging the model to produce input representations that are discriminable across key input attributes, such as sentiment polarity and the existence of implicit opinions and aspects. Second, we introduce GEN-NAT, a new structured generation format that better adapts autoregressive encoder-decoder models to extract quadruples in a generative fashion. Experimental results show that GEN-SCL-NAT achieves top performance across three ACOS datasets, averaging 1.48% F1 improvement, with a maximum 1.73% increase on the LAPTOP-L1 dataset. Additionally, we see significant gains on implicit aspect and opinion splits that have been shown as challenging for existing ACOS approaches.
