Seq2Seq-VIS : A Visual Debugging Tool for Sequence-to-Sequence Models
Published at
VAST
| Berlin, Germany
2018
- Best Paper Honorable Mention
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 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 analysis tool that allows interaction and "what
if"-style exploration of trained sequence-to-sequence models through each stage
of the translation process. The aim is to identify which patterns have been
learned, to detect model errors, and to probe the model with counterfactual
scenario. We demonstrate the utility of our tool through several real-world
sequence-to-sequence use cases on large-scale models.