Summary of Rethinking Aste: a Minimalist Tagging Scheme Alongside Contrastive Learning, by Qiao Sun et al.
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning
by Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, Qinying Gu
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this research, we propose a novel approach to Aspect Sentiment Triplet Extraction (ASTE), a subtask of fine-grained sentiment analysis that aims to extract structured sentiment triplets from unstructured textual data. Our method employs a contrastive learning approach and a tagging scheme to mitigate challenges posed by existing ASTE approaches. This allows for comparable or superior performance compared to state-of-the-art techniques, while reducing computational overhead. Notably, our method outperforms Large Language Models (LLMs) like GPT 3.5 and GPT 4 in few-shot learning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ASTE is a task that involves extracting structured sentiment triplets from unstructured text data. The goal is to identify how people feel about certain aspects of something, like products or services. This research proposes a new way to do this using a tagging scheme and contrastive learning approach. The method works well and is more efficient than other approaches. It even performs better than large language models in some cases. |
Keywords
» Artificial intelligence » Few shot » Gpt
