@article {strobelt2018seq2seq,
	title = {Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	year = {2018},
	abstract = {sequence-to-sequence models have proven to beaccurate and robust for many sequence prediction tasks, and havebecome the standard approach for automatic translation of text. Themodels work with a five-stage blackbox pipeline that begins with encoding asource sequence to a vector space and then decoding out to a newtarget sequence. This process is now standard, but like many deeplearning methods remains quite difficult to understand or debug. \&nbsp;Inthis work, we present a visual analysis toolthat allows interaction and "what if"-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identifywhich patterns have been learned, to detect model errors, and to probe the model withcounterfactual scenario. We demonstrate the utility of our tool through several real-world \&nbsp;sequence-to-sequence use cases on large-scale models.},
	url = {https://ieeexplore.ieee.org/abstract/document/8494828/},
	author = {Hendrik Strobelt and Sebastian Gehrmann and Behrisch, Michael and Perer, Adam and Pfister, Hanspeter and Rush, Alexander M}
}
