Answers.Body: <p>Seq2Seq and Bidirectional RNNs are not doing the same thing, at least in their classic form.</p>
  <p>Seq2Seq models are used to generate a sequence from another sequence. Consider, for example, the translation task from one language to another. In that sense, Seq2Seq is more a family of models, not an architecture.</p>
  <p>On the other hand, the Bidirectional RNN is a neural network architecture and can be used to build several models including Seq2Seq, for example, the encoding part of the Seq2Seq can be a Bi-RNN, but they also can be used for other tasks, for example, sentence classification or sentiment analysis.</p>
  
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Answers.CreationDate: 2020-03-03T09:05:48.747Z
Answers.LastActivityDate: 2021-11-24T11:48:05.677Z
Answers.LastEditDate: 2021-11-24T11:48:05.677Z
Answers.UserDisplayName: hola
Body: <p>It seems to me that Seq2Seq models and Bidirectional RNNs try to do the same thing. Is that true?</p>
  <p>Also, when would you recommend one setup over another?</p>
  
Tags: <comparison><recurrent-neural-networks><seq2seq><bidirectional-rnn>
Title: Do Seq2Seq models and the Bidirectional RNN do the same thing?
UserDisplayName: ADSBJason
ThreadText: # Do Seq2Seq models and the Bidirectional RNN do the same thing?
  
  (Asked by: username_0 on 2020-03-03)
  
  It seems to me that Seq2Seq models and Bidirectional RNNs try to do the same thing. Is that true?
  
  Also, when would you recommend one setup over another?
  
  **Tags:** comparison, recurrent-neural-networks, seq2seq, bidirectional-rnn
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-03-03. Score: 1
  
  Seq2Seq and Bidirectional RNNs are not doing the same thing, at least in their classic form.
  
  Seq2Seq models are used to generate a sequence from another sequence. Consider, for example, the translation task from one language to another. In that sense, Seq2Seq is more a family of models, not an architecture.
  
  On the other hand, the Bidirectional RNN is a neural network architecture and can be used to build several models including Seq2Seq, for example, the encoding part of the Seq2Seq can be a Bi-RNN, but they also can be used for other tasks, for example, sentence classification or sentiment analysis.
  
  
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