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This paper focuses on the task of sentiment transfer on non-parallel text,which modifies sentiment attributes (e.g., positive or negative) of sentenceswhile preserving their attribute-independent content. Due to the limitedcapability of RNNbased encoder-decoder structure to capture deep and long-rangedependencies among words, previous works can hardly generate satisfactorysentences from scratch. When humans convert the sentiment attribute of asentence, a simple but effective approach is to only replace the originalsentimental tokens in the sentence with target sentimental expressions, insteadof building a new sentence from scratch. Such a process is very similar to thetask of Text Infilling or Cloze, which could be handled by a deep bidirectionalMasked Language Model (e.g. BERT). So we propose a two step approach "Mask andInfill". In the mask step, we separate style from content by masking thepositions of sentimental tokens. In the infill step, we retrofit MLM toAttribute Conditional MLM, to infill the masked positions by predicting wordsor phrases conditioned on the context1 and target sentiment. We evaluate ourmodel on two review datasets with quantitative, qualitative, and humanevaluations. Experimental results demonstrate that our models improvestate-of-the-art performance.

"Mask and Infill" : Applying Masked Language Model to Sentiment Transfer