Implementation can be found in the code. Dropout Layers Dropout layers were added to the feedforward neural net in each encoder layer, each decoder layer, as well as the classifier modules. However, ...

the first embedding (i.e. the decoder embedding) --> dimension (B, T1, E) x2: the second embedding (i.e. the encoder's embedding in the case of Transformer decoder) --> dimension (B, T2, E) ...

Abstract: Solving math word problems is a popular topic in natural language processing. We not only need to classify the grammatical structures in the questions, but also understand the mathematical ...

The ability of transformers to handle data sequences without the need for sequential processing makes them extremely effective for various NLP tasks, including translation, text summarization, and ...