Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange
TL
TL;DR
A novel method for measurably adjusting the semantics of text while preserving its sentiment and fluency, a task the authors call semantic text exchange is presented and a pipeline called SMERTI is introduced that combines entity replacement, similarity masking, and text infilling.
Abstract
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.
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Transformer-XL: Attentive Language Models beyond a Fixed-Length Context
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Text Infilling
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Universal Sentence Encoder for English
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Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
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