Summary of Minicongts: a Near Ultimate Minimalist Contrastive Grid Tagging Scheme For Aspect Sentiment Triplet Extraction, by Qiao Sun et al.
MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction
by Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, Qinying Gu
First submitted to arxiv on: 17 Jun 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 This paper proposes a novel approach, Aspect Sentiment Triplet Extraction (ASTE), to co-extract sentiment triplets from a given corpus. The method integrates a minimalist tagging scheme and token-level contrastive learning strategy into pre-trained representations, achieving comparable or superior performance to state-of-the-art techniques while reducing computational overhead. The proposed approach also evaluates GPT-4’s performance in few-shot learning and Chain-of-Thought scenarios for this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to find sentiment triplets in text. Sentiment triplets are like three-part puzzles where you have an aspect, a sentiment, and some text that shows the sentiment. The current methods for finding these triplets are either very complicated or rely on extra information. This study is the first to look at how we can use pre-trained language models, like GPT-4, to find sentiment triplets without needing all that extra work. It shows that even with really large language models, this approach still works well. |
Keywords
* Artificial intelligence * Few shot * Gpt * Token
