The encoder-decoder structure is especially useful for NLP tasks that involve generating text from text, such as machine translation, text summarization, text generation, and more.

Model Architecture Encoder-Decoder Architecture The encoder-decoder architecture is commonly used in sequence-to-sequence tasks like machine translation. Here's how it works: The encoder processes the ...

This repository showcases an advanced implementation of a Neural Machine Translation (NMT) model using the Encoder-Decoder architecture with Long Short-Term Memory (LSTM) networks. The model is ...

Encoder-Decoder Architecture. Based on the vanilla Transformer model, the encoder-decoder architecture consists of two stacks: an encoder and a decoder. The encoder uses stacked multi-head ...

Abstract—Recently, universal neural machine translation (NMT) with shared encoder-decoder gained good performance on zero-shot translation. Unlike universal NMT, jointly trained language-specific ...

Armed with attention mechanism, the recurrent neural network-based encoder-decoder model (or sequence to sequence model) has become the standard architecture to tackle many sequence Nature Language ...

Let's use machine translation as an example to illustrate the principle of a language model with an encoder-decoder architecture. Our task is to translate a French sentence into an English one. Of ...

Encoder-Decoder Architectures. Encoder-decoder architectures are a broad category of models used primarily for tasks that involve transforming input data into output data of a different form or ...