LAST at SemEval-2020 Task 10: Finding Tokens to Emphasise in Short Written Texts with Precomputed Embedding Models and LightGBM
To select tokens to be emphasised in short texts, a system mainly based on precomputed embedding models, such as BERT and ELMo, and LightGBM is proposed. Its performance is low. Additional analyzes suggest that its effectiveness is poor at predicting the highest emphasis scores while they are the most important for the challenge and that it is very sensitive to the specific instances provided during learning.
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Methods
Adam •
Attention Dropout •
BERT •
BiLSTM •
Dense Connections •
Dropout •
ELMo •
GELU •
Layer Normalization •
Linear Layer •
Linear Warmup With Linear Decay •
LSTM •
Multi-Head Attention •
Residual Connection •
Scaled Dot-Product Attention •
Sigmoid Activation •
Softmax •
Tanh Activation •
Weight Decay •
WordPiece
