Aspect Sentiment Triplet Extraction (ASTE) is a challenging task in sentiment analysis, aiming to provide fine-grained insights into human sentiments. However, existing benchmarks are limited to two domains and do not evaluate model performance on unseen domains, raising concerns about the generalization of proposed methods. Furthermore, it remains unclear if large language models (LLMs) can effectively handle complex sentiment tasks like ASTE. In this work, we address the issue of generalization in ASTE from both a benchmarking and modeling perspective. We introduce a domain-expanded benchmark by annotating samples from diverse domains, enabling evaluation of models in both in-domain and out-of-domain settings. Additionally, we propose CASE, a simple and effective decoding strategy that enhances trustworthiness and performance of LLMs in ASTE. Through comprehensive experiments involving multiple tasks, settings, and models, we demonstrate that CASE can serve as a general decoding strategy for complex sentiment tasks. By expanding the scope of evaluation and providing a more reliable decoding strategy, we aim to inspire the research community to reevaluate the generalizability of benchmarks and models for ASTE. Our code, data, and models are available at https://github.com/DAMO-NLP-SG/domain-expanded-aste.

翻译：方面情感三元组提取（ASTE）是情感分析中一项具有挑战性的任务，旨在提供对人类情感的细粒度洞察。然而，现有基准仅限于两个领域，且未评估模型在未见领域上的性能，这引发了关于所提方法泛化能力的担忧。此外，大型语言模型（LLMs）是否能有效处理如ASTE这类复杂情感任务仍不明确。在本工作中，我们从基准构建和模型设计两个角度探讨ASTE中的泛化问题。我们通过标注来自多个领域的样本，引入了一个领域扩展的基准，从而支持模型在领域内和跨领域设置下的评估。此外，我们提出了CASE，一种简单而有效的解码策略，可提升LLMs在ASTE任务中的可信度与性能。通过涵盖多任务、多设置和多模型的综合实验，我们证明CASE可作为复杂情感任务的通用解码策略。通过扩展评估范围并提供更可靠的解码策略，我们旨在启发研究社区重新审视ASTE基准与模型的泛化能力。我们的代码、数据与模型公开于https://github.com/DAMO-NLP-SG/domain-expanded-aste。