Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models

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.

Made by:

IBM Research, Cambridge

MIT-IBM Watson AI Lab

Cite

@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 }

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