Seq2SeqPy: A Lightweight and Customizable Toolkit for Neural Sequence-to-Sequence Modeling

We present Seq2SeqPy a lightweight toolkit for sequence-to-sequence modeling that prioritizes simplicity and ability to customize the standard architectures easily. The toolkit supports several known architectures such as Recurrent Neural Networks, Pointer Generator Networks, and transformer model... (read more)

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Methods used in the Paper


METHOD TYPE
Residual Connection
Skip Connections
Label Smoothing
Regularization
Multi-Head Attention
Attention Modules
Adam
Stochastic Optimization
ReLU
Activation Functions
Dropout
Regularization
BPE
Subword Segmentation
Dense Connections
Feedforward Networks
Layer Normalization
Normalization
Softmax
Output Functions
Scaled Dot-Product Attention
Attention Mechanisms
Transformer
Transformers