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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 41–50 of 52 papers

TitleStatusHype
FOAL: Fine-grained Contrastive Learning for Cross-domain Aspect Sentiment Triplet Extraction—0
Nonautoregressive Encoder-Decoder Neural Framework for End-to-End Aspect-Based Sentiment Triplet Extraction—0
Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish—0
Pretrained Language Encoders are Natural Tagging Frameworks for Aspect Sentiment Triplet Extraction—0
Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive Learning—0
SAMBERT: Improve Aspect Sentiment Triplet Extraction by Segmenting the Attention Maps of BERT—0
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction—0
Table-Filling via Mean Teacher for Cross-domain Aspect Sentiment Triplet Extraction—0
Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)—0
Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction—0
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