Existing works on Aspect Sentiment Triplet Extraction (ASTE) explicitly focus on developing more efficient fine-tuning techniques for the task. Instead, our motivation is to come up with a generic approach that can improve the downstream performances of multiple ABSA tasks simultaneously. Towards this, we present CONTRASTE, a novel pre-training strategy using CONTRastive learning to enhance the ASTE performance. While we primarily focus on ASTE, we also demonstrate the advantage of our proposed technique on other ABSA tasks such as ACOS, TASD, and AESC. Given a sentence and its associated (aspect, opinion, sentiment) triplets, first, we design aspect-based prompts with corresponding sentiments masked. We then (pre)train an encoder-decoder model by applying contrastive learning on the decoder-generated aspect-aware sentiment representations of the masked terms. For fine-tuning the model weights thus obtained, we then propose a novel multi-task approach where the base encoder-decoder model is combined with two complementary modules, a tagging-based Opinion Term Detector, and a regression-based Triplet Count Estimator. Exhaustive experiments on four benchmark datasets and a detailed ablation study establish the importance of each of our proposed components as we achieve new state-of-the-art ASTE results.

翻译：现有关于方面情感三元组提取（ASTE）的研究明确专注于为该任务开发更高效的微调技术。相反，我们的动机是提出一种通用方法，能够同时提升多个ABSA任务的下游性能。为此，我们提出CONTRASTE——一种采用对比学习来增强ASTE性能的新型预训练策略。尽管我们主要关注ASTE，但也展示了所提方法在其他ABSA任务（如ACOS、TASD和AESC）上的优势。给定一个句子及其关联的（方面、观点、情感）三元组，首先，我们设计基于方面的提示，并掩码对应的情感标签。然后，我们通过对解码器生成的掩码术语的方面感知情感表示应用对比学习，来（预）训练一个编码器-解码器模型。为微调由此获得的模型权重，我们进一步提出一种新颖的多任务方法：将基础编码器-解码器模型与两个互补模块（基于标注的观点术语检测器和基于回归的三元组计数估计器）相结合。在四个基准数据集上的详尽实验和详细的消融研究，验证了我们提出的每个组件的重要性，并取得了新的ASTE最佳结果。