  In this paper, we present a novel method for measurably adjusting the
semantics of text while preserving its sentiment and fluency, a task we call
semantic text exchange. This is useful for text data augmentation and the
semantic correction of text generated by chatbots and virtual assistants. We
introduce a pipeline called SMERTI that combines entity replacement, similarity
masking, and text infilling. We measure our pipeline's success by its Semantic
Text Exchange Score (STES): the ability to preserve the original text's
sentiment and fluency while adjusting semantic content. We propose to use
masking (replacement) rate threshold as an adjustable parameter to control the
amount of semantic change in the text. Our experiments demonstrate that SMERTI
can outperform baseline models on Yelp reviews, Amazon reviews, and news
headlines.
