MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction
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...
| Authors | Ao Hong, Lehang Wang, Zhirun Yue et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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