The Aspect Sentiment Triplet Extraction (ASTE) task aims to extract aspect terms, opinion terms, and their corresponding sentiment polarity from a given sentence. It remains one of the most prominent subtasks in fine-grained sentiment analysis. Most existing approaches frame triplet extraction as a 2D table-filling process in an end-to-end manner, focusing primarily on word-level interactions while often overlooking sentence-level representations. This limitation hampers the model's ability to capture global contextual information, particularly when dealing with multi-word aspect and opinion terms in complex sentences. To address these issues, we propose boundary-driven table-filling with cross-granularity contrastive learning (BTF-CCL) to enhance the semantic consistency between sentence-level representations and word-level representations. By constructing positive and negative sample pairs, the model is forced to learn the associations at both the sentence level and the word level. Additionally, a multi-scale, multi-granularity convolutional method is proposed to capture rich semantic information better. Our approach can capture sentence-level contextual information more effectively while maintaining sensitivity to local details. Experimental results show that the proposed method achieves state-of-the-art performance on public benchmarks according to the F1 score.

翻译：方面情感三元组抽取（ASTE）任务旨在从给定句子中提取方面词、观点词及其对应的情感极性。它仍然是细粒度情感分析中最突出的子任务之一。现有方法大多将三元组抽取建模为端到端的二维表格填充过程，主要关注词级交互，而往往忽略句子级表征。这一局限削弱了模型捕捉全局上下文信息的能力，尤其在处理复杂句子中的多词方面词和观点词时更为明显。为解决这些问题，我们提出边界驱动的跨粒度对比学习表格填充方法（BTF-CCL），以增强句子级表征与词级表征之间的语义一致性。通过构建正负样本对，模型被迫同时学习句子级和词级的关联。此外，我们提出一种多尺度、多粒度的卷积方法，以更好地捕捉丰富的语义信息。我们的方法在保持对局部细节敏感性的同时，能更有效地捕获句子级上下文信息。实验结果表明，根据F1分数，所提方法在公开基准测试中达到了最先进的性能。