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Aspect Sentiment Triplet Extraction

Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.

Papers

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Showing 1–10 of 52 papers

TitleStatusHype
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet ExtractionCode1
A semantically enhanced dual encoder for aspect sentiment triplet extractionCode1
Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet ExtractionCode1
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple PredictionCode1
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet ExtractionCode1
Structural Bias for Aspect Sentiment Triplet ExtractionCode1
Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment AnalysisCode1
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet ExtractionCode1
A Span-level Bidirectional Network for Aspect Sentiment Triplet ExtractionCode1
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