In this section, we will demonstrate the application of an encoder–decoder architecture, where both the encoder and decoder are implemented as RNNs, to the task of machine translation …

Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.

Oct 21, 2022 · In this work, we investigate the aforementioned concept for machine translation. Specifically, we experiment with bilingual translation, translation with additional target …

Through extensive experiments in various translation directions, considering back-translation and multilingual translation, we find that an encoder-only model can perform as good as an …

So the Sequence to Sequence (seq2seq) model in this post uses an encoder-decoder architecture, which uses a type of RNN called LSTM (Long Short Term Memory), where the …

Encoder-Decoder: (Neural) model that takes in a sequence of discrete symbols and generates another sequence.

In this project, we build a deep neural network that functions as part of a machine translation pipeline. The pipeline accepts English text as input and returns the French translation.

Q: Are encoder-decoder models only used for machine translation? A: No, encoder-decoder models, also known as sequence-to-sequence models, are employed in various natural …

Explore the evolution of translation from RNNs to Transformers, highlighting breakthroughs in accuracy and efficiency.

Abstract nt approach to machine translation. Neural machine translation uses an artificial neural network to learn and perform transla-tions. Usually an encoder-decoder architec