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.
@ARTICLE{seq2seqvisv1,
author = {{Strobelt}, H. and {Gehrmann}, S. and {Behrisch}, M. and {Perer}, A. and {Pfister}, H. and {Rush}, A.~M.},
title = "{Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1804.09299v1},
primaryClass = "cs.CL",
keywords = {Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing},
year = 2018,
month = April
}