MASTE：一种用于零样本方面情感三元组提取的多智能体管道
MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction
AI总结：
研究针对方面情感三元组提取问题，提出多智能体管道MASTE，将其分解为四个阶段，由专门智能体处理不同子任务，实现完全无训练的零样本提取，在多个基准测试中显著优于基线，缩小与全监督方法差距。
摘要：
方面情感三元组提取（ASTE）需要从给定的评论句子中联合识别（方面、观点、情感）三元组。虽然大语言模型（LLMs）在许多自然语言处理基准测试中实现了强大的零样本性能，但它们在ASTE上的有效性仍然有限。常见的补救措施，如少样本上下文学习和思维链提示，只提供了边际改进。受经典智能体范式的启发，我们提出了MASTE，一种用于零样本方面情感三元组提取的多智能体管道。MASTE将ASTE分解为四个连续阶段，专门的智能体处理不同的组合子任务。实验表明，MASTE在相同骨干下显著优于零样本和思维链LLM基线，缩小了与完全监督方法的差距。
英文摘要：
Aspect Sentiment Triplet Extraction (ASTE) requires jointly identifying (aspect, opinion, sentiment) triples from a given review sentence. While large language models (LLMs) achieve strong zero-shot performance on many NLP benchmarks, their effectiveness on ASTE remains limited, as single-pass generation forces the model to determine span boundaries, opinion grouping, and sentiment polarity in a single decoding step. Common remedies, such as few-shot in-context learning and chain-of-thought prompting, offer only marginal improvements and rely heavily on either in-domain demonstrations sampled from labeled training data or carefully engineered reasoning prompts, neither of which is broadly available in zero-shot deployment. Inspired by the classical agent paradigm, we propose MASTE, a multi-agent pipeline for zero-shot Aspect Sentiment Triplet Extraction. MASTE decomposes ASTE into four sequential stages, where specialized agents handle different compositional subtasks with explicit conditioning on prior outputs. This design enables entirely training-free zero-shot ASTE and generalizes across different backbones and datasets. Extensive experiments on four ASTE benchmarks show that MASTE substantially outperforms zero-shot and chain-of-thought LLM baselines under the same backbone, narrowing the gap to fully supervised methods without using any labeled triplets. Code is available at https://github.com/Hankerlove/MASTE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
