Seq2Seq - anwarvic.github.io
anwarvic.github.io › machine-translation › Seq2SeqSep 10, 2014 · Seq2Seq. Sequence-to-sequence (seq2seq) models or encoder-decoder architecture, created by IlyaSutskever and published in their paper: Sequence to Sequence Learning with Neural Networks published in 2014, have enjoyed great success in a machine translation, speech recognition, and text summarization.
Sequence to Sequence Learning with Neural Networks
papers.nips.cc › paper › 2014sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method usesamultilayeredLongShort-TermMemory(LSTM)tomaptheinputsequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.
[1705.03122] Convolutional Sequence to ... - arXiv.org
https://arxiv.org/abs/1705.0312208.05.2017 · The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. Compared to recurrent models, computations over all elements can be fully parallelized during training and optimization is easier since the …
Seq2Seq Explained - Papers With Code
https://paperswithcode.com/method/seq2seqSeq2Seq, or Sequence To Sequence, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one LSTM, the encoder, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the decoder, to extract the …
Seq2Seq Explained | Papers With Code
paperswithcode.com › method › seq2seqSeq2Seq, or Sequence To Sequence, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one LSTM, the encoder, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the decoder, to extract the output sequence from that vector.
Seq2seq and Attention - GitHub Pages
lena-voita.github.io › nlp_course › seq2seq_andThe paper Sequence to Sequence Learning with Neural Networks introduced an elegant trick to make simple LSTM seq2seq models work better: reverse the order of the source tokens (but not the target). After that, a model will have many short-term connections: the latest source tokens it sees are the most relevant for the beginning of the target.
Sequence to Sequence Learning with Neural Networks - arXiv.org
https://arxiv.org/abs/1409.321510.09.2014 · Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the …
[1409.3215] Sequence to Sequence Learning with Neural Networks
arxiv.org › abs › 1409Sep 10, 2014 · Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. Although DNNs work well whenever large labeled training sets are available, they cannot be used to map sequences to sequences. In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses ...