The authors show that standard text style-transfer metrics are unstable and manipulable, recommend BLEU against human rewrites as an additional benchmark, and report three architectures that improve on that metric.
What is wrong with style transfer for texts?
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abstract
A number of recent machine learning papers work with an automated style transfer for texts and, counter to intuition, demonstrate that there is no consensus formulation of this NLP task. Different researchers propose different algorithms, datasets and target metrics to address it. This short opinion paper aims to discuss possible formalization of this NLP task in anticipation of a further growing interest to it.
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cs.CL 1years
2019 1verdicts
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Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites
The authors show that standard text style-transfer metrics are unstable and manipulable, recommend BLEU against human rewrites as an additional benchmark, and report three architectures that improve on that metric.