Summary of Pgso: Prompt-based Generative Sequence Optimization Network For Aspect-based Sentiment Analysis, by Hao Dong et al.
PGSO: Prompt-based Generative Sequence Optimization Network for Aspect-based Sentiment Analysis
by Hao Dong, Wei Wei
First submitted to arxiv on: 1 Dec 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 The paper introduces two optimization strategies for generative pre-training models on Aspect-based Sentiment Analysis (ABSA) tasks, addressing the issue of struggling with long texts and implicit relations. The rule-based static optimization reorders context based on dependency relation priority, while the score-based dynamic optimization dynamically regulates contextual sequence using neural network scores. A unified Prompt-based Generative Sequence Optimization network (PGSO) is proposed, jointly optimizing training targets and generative models. PGSO consists of prompt construction and sequence regulator components, utilizing semantic, syntactic, and original-sequence information to regulate context. Experimental results on four ABSA tasks across multiple benchmarks show an average F1 score improvement of 3.52% over state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a problem with generative models for sentiment analysis. It’s hard for these models to understand long texts and the connections between words. The researchers suggest two ways to improve this: one is based on rules, and the other uses a neural network to decide what order the words should be in. They also propose a new model that combines both of these ideas. This new model can be used for different types of sentiment analysis tasks and has been shown to perform well. |
Keywords
» Artificial intelligence » F1 score » Neural network » Optimization » Prompt
