Oct 31, 2019 · The encoder-decoder model is a way of using recurrent neural networks for sequence-to-sequence prediction problems. It was initially developed for machine translation problems, although it has...

Image-centric translation can be used for example to use OCR of the text on a phone camera image as input to an MT system to translate menus or street signs. The standard algorithm for MT is the encoder-decoder network, also called the sequence to sequence network, an architecture that can be implemented with RNNs or with Transformers.

Aug 7, 2019 · The Encoder-Decoder architecture with recurrent neural networks has become an effective and standard approach for both neural machine translation (NMT) and sequence-to-sequence (seq2seq) prediction in general.

A Sequence to Sequence network, or seq2seq network, or Encoder Decoder network, is a model consisting of two RNNs called the encoder and decoder. The encoder reads an input sequence and outputs a single vector, and the decoder reads that vector to produce an output sequence.

Aug 16, 2024 · The Encoder-Decoder architecture is a fundamental concept in machine learning, especially in tasks involving sequences such as machine translation, text summarization, and image captioning.

Aug 7, 2019 · Encoder-Decoder Model for Neural Machine Translation. The Encoder-Decoder architecture for recurrent neural networks is displacing classical phrase-based statistical machine translation systems for state-of-the-art results.

Jun 3, 2014 · The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model.

Aug 7, 2019 · The encoder-decoder model provides a pattern for using recurrent neural networks to address challenging sequence-to-sequence prediction problems, such as machine translation. Encoder-decoder models can be developed in the Keras Python deep learning library and an example of a neural machine translation system developed with this model has been ...

Nov 19, 2020 · There are multiple tasks that can be solved by using seq2seq modeling, including text summarization, speech recognition, image and video captioning, and question answering. It can also be used in genomics for DNA sequence modeling. A seq2seq model has two parts: an encoder and a decoder.

Oct 11, 2024 · The results showed that encoder-decoder models generally outperformed their decoder-only counterparts in translation quality and contextual understanding. However, decoder-only models demonstrated significant advantages in computational efficiency and fluency.