Subjectivity Analysis On Subj

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
RoBERTa+DualCL	97.34	Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation
BERT-Base + CLR + LSTM	97.30	An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks
RoBERTa-large 355M + Entailment as Few-shot Learner	97.1	Entailment as Few-Shot Learner
BERT-Base + LSTM	96.60	An Empirical Evaluation of Word Embedding Models for Subjectivity Analysis Tasks
AdaSent	95.50	Self-Adaptive Hierarchical Sentence Model
VLAWE	95.0	Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation
CNN+MCFA	94.80	Translations as Additional Contexts for Sentence Classification
byte mLSTM7	94.7	A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
Byte mLSTM	94.60	Learning to Generate Reviews and Discovering Sentiment
USE	93.90	Universal Sentence Encoder
Capsule-B	93.8	Investigating Capsule Networks with Dynamic Routing for Text Classification
Fast Dropout	93.60	-
SWEM-concat	93	Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
STM+TSED+PT+2L	92.34	The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning
GRU-RNN-GLOVE	91.85	All-but-the-Top: Simple and Effective Postprocessing for Word Representations
SDAE	90.8	Learning Distributed Representations of Sentences from Unlabelled Data

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