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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 21–30 of 52 papers

TitleStatusHype
UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning—0
A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis—0
A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction—0
A Pairing Enhancement Approach for Aspect Sentiment Triplet Extraction—0
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment—0
Boundary-Driven Table-Filling with Cross-Granularity Contrastive Learning for Aspect Sentiment Triplet Extraction—0
Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction—0
Explicit Interaction Network for Aspect Sentiment Triplet Extraction—0
First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction—0
FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction—0
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