A syntax-aware prompt-tuning framework that improves aspect-based sentiment analysis using pretrained language models.
Dependency-tree prompt mining selects contextual words as prompts using syntactic distance and relation matrices. An auto prompt framework performs gradient-based discrete token selection while a soft prompt framework learns continuous pseudo-token embeddings through a BiLSTM and MLP encoder — outperforming baselines on SemEval Restaurant, Laptop and Twitter datasets.
Prompt tuning for aspect-based sentiment analysis usually ignores sentence structure, so prompts attach to words that are lexically near the aspect but syntactically unrelated to it.
Mine prompts from the dependency tree so selection is driven by syntactic relationships, and support both discrete and continuous prompt learning within the same framework.
Dependency-Tree Prompt Mining
Selects contextual words as prompts using syntactic distance and relation matrices.
Auto Prompt Framework
Gradient-based discrete prompt token selection.
Soft Prompt Framework
BiLSTM + MLP encoder learns continuous pseudo-token embeddings.
Benchmarked Evaluation
Outperforms baselines on SemEval Restaurant, Laptop, and Twitter datasets.
Deep Learning / NLP
Syntax
Evaluation
Dependency parsing
Sentences are parsed into dependency trees.
Prompt mining
Syntactically relevant context words are selected as prompts.
Prompt learning
Discrete and soft prompt frameworks are trained.
Sentiment prediction
Aspect-level sentiment is predicted.
Benchmarking
Results are compared against SemEval baselines.
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