• DOI:10.1109/TVCG.2018.2865044
Corpus ID: 13754931

Seq2seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models

@article{Strobelt2019Seq2seqVisAV,
  title={Seq2seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models},
  author={Hendrik Strobelt and Sebastian Gehrmann and Michael Behrisch and Adam Perer and H. Pfister and Alexander M. Rush},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2019},
  volume={25},
  pages={353-363}
}

Hendrik Strobelt, Sebastian Gehrmann, +3 authors Alexander M. Rush

Published 2019

Computer Science, Medicine

IEEE Transactions on Visualization and Computer Graphics

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 with a five-stage blackbox pipeline that begins with 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… CONTINUE READING

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