@article{strobelt2018s,
  title={Seq2Seq-Vis: A visual debugging tool for sequence-to-sequence models},
  author={Strobelt, Hendrik and Gehrmann, Sebastian and Behrisch, Michael and Perer, Adam and Pfister, Hanspeter and Rush, Alexander M},
  journal={IEEE transactions on visualization and computer graphics},
  volume={25},
  number={1},
  pages={353--363},
  year={2018},
  publisher={IEEE},
  doi={10.1109/TVCG.2018.2865044},
abstract={Neural Sequence-to-Sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work in a five stage blackbox process that involves encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction with a trained sequence-to-sequence model through each stage of the translation process. The aim is to identify which patterns have been learned and to detect model errors.}
}