A. Building seq2seq models with Tensorflow (v1.0.0)

• seq2seq-tutorial01: basic architecture setup.
• seq2seq-tutorial02: Sutskever et al. (2014).
• seq2seq-tutorial03: Bahdanau et al. (2015).
• Enc-Dec-for-sorting-strings: Bahdanau et al. (2015).
• Ptr-Net-for-sorting-strings: Vinyals et al. (2016).
• Ptr-Net-for-sorting-sentences: Gong et al. (2016)

B. Building seq2seq models with PyTorch (v0.3.1)

• PyTorch-seq2seq-full-tutorial: Prakash et al. (2016), with toy data.
  • Bidirectional
  • Stacking
  • Residual links
  • Attention (Bahdanau or Luong)
• PyTorch-seq2seq-mscoco* (data = MSCOCO): Prakash et al. (2016)
  • Base: greedy search.
  • -beam: beam search.
• PyTorch-seq2seq-transformer: Vaswani et al. (2017)

NB: the PyTorch code uses a few helpers, which are stored in utils.py and seq2seq_data_loader.py NB: to download the data used in the demos, go to https://github.com/iamaaditya/neural-paraphrase-generation/tree/dev/data, create a dir "mscoco/", and put the following files under it: train_source.txt, train_target.txt, test_source.txt, test_target.txt.