Sentiment Analysis On Sst 5 Fine Grained

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
Heinsen Routing + RoBERTa Large	59.8	An Algorithm for Routing Vectors in Sequences
RoBERTa-large+Self-Explaining	59.1	Self-Explaining Structures Improve NLP Models
Heinsen Routing + GPT-2	58.5	An Algorithm for Routing Capsules in All Domains
BCN+Suffix BiLSTM-Tied+CoVe	56.2	Improved Sentence Modeling using Suffix Bidirectional LSTM
BERT Large	55.5	Fine-grained Sentiment Classification using BERT
LM-CPPF RoBERTa-base	54.9	LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning
BCN+ELMo	54.7	Deep contextualized word representations
byte mLSTM7	54.6	A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors
BCN+Char+CoVe	53.7	Learned in Translation: Contextualized Word Vectors
Bi-CAS-LSTM	53.6	Cell-aware Stacked LSTMs for Modeling Sentences
CNN-RNF-LSTM	53.4	Convolutional Neural Networks with Recurrent Neural Filters
BERT Base	53.2	Fine-grained Sentiment Classification using BERT
Star-Transformer	53.0	Star-Transformer
BP-Transformer + GloVe	52.71	BP-Transformer: Modelling Long-Range Context via Binary Partitioning
MEAN	51.4	A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification
Constituency Tree-LSTM	51.0	Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Bi-LSTM+2+5	50.4	Leveraging Multi-grained Sentiment Lexicon Information for Neural Sequence Models
MPAD-path	49.68	Message Passing Attention Networks for Document Understanding
Epic	49.6	-
RNN-Capsule	49.3	Sentiment Analysis by Capsules

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