Language Representation Models for Fine-Grained Sentiment Classification

27 May 2020 Brian Cheang Bailey Wei David Kogan Howey Qiu Masud Ahmed

Sentiment classification is a quickly advancing field of study with applications in almost any field. While various models and datasets have shown high accuracy inthe task of binary classification, the task of fine-grained sentiment classification is still an area with room for significant improvement... (read more)

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Methods used in the Paper


METHOD TYPE
DistilBERT
Transformers
Weight Decay
Regularization
Softmax
Output Functions
Adam
Stochastic Optimization
Dropout
Regularization
GELU
Activation Functions
Attention Dropout
Regularization
Linear Warmup With Linear Decay
Learning Rate Schedules
Dense Connections
Feedforward Networks
Layer Normalization
Normalization
Scaled Dot-Product Attention
Attention Mechanisms
WordPiece
Subword Segmentation
Residual Connection
Skip Connections
BERT
Language Models
Multi-Head Attention
Attention Modules
LAMB
Large Batch Optimization
ALBERT
Transformers
RoBERTa
Transformers