Below is a comprehensive table of key terms used in the paper “Attention is All You Need,” along with their English and Chinese translations. Where applicable, links to external resources are provided for further reading.

English Term	Chinese Translation	Explanation	Link
Encoder	编码器	The component that processes input sequences.
Decoder	解码器	The component that generates output sequences.
Attention Mechanism	注意力机制	Measures relationships between sequence elements.	Attention Mechanism Explained
Self-Attention	自注意力	Focuses on dependencies within a single sequence.
Masked Self-Attention	掩码自注意力	Prevents the decoder from seeing future tokens.
Multi-Head Attention	多头注意力	Combines multiple attention layers for better modeling.
Positional Encoding	位置编码	Adds positional information to embeddings.
Residual Connection	残差连接	Shortcut connections to improve gradient flow.
Layer Normalization	层归一化	Stabilizes training by normalizing inputs.	Layer Normalization Details
Feed-Forward Neural Network (FFNN)	前馈神经网络	Processes data independently of sequence order.	Feed-Forward Networks in NLP
Recurrent Neural Network (RNN)	循环神经网络	Processes sequences step-by-step, maintaining state.	RNN Basics
Convolutional Neural Network (CNN)	卷积神经网络	Uses convolutions to extract features from input data.	CNN Overview
Parallelization	并行化	Performing multiple computations simultaneously.
BLEU (Bilingual Evaluation Understudy)	双语评估替代	A metric for evaluating the accuracy of translations.	Understanding BLEU

This table provides a solid foundation for understanding the technical terms used in the “Attention is All You Need” paper. If you have questions or want to dive deeper into any term, the linked resources are a great place to start!