Contextual Text Style Transfer
Yu Cheng, Zhe Gan, Yizhe Zhang, Oussama Elachqar, Dianqi Li, Jun Liu · 2020
We introduce a new task, Contextual Text Style Transfer -translating a sentence into a desired style with its surrounding context taken into account.This brings two key challenges to existing style transfer approaches: (i) how to preserve the semantic meaning of target sentence and its consistency with surrounding context during transfer; (ii) how to train a robust model with limited labeled data accompanied by context.To realize high-quality style transfer with natural context preservation, we propose a Context-Aware Style Transfer (CAST) model, which uses two separate encoders for each input sentence and its surrounding context.A classifier is further trained to ensure contextual consistency of the generated sentence.To compensate for the lack of parallel data, additional selfreconstruction and back-translation losses are introduced to leverage non-parallel data in a semi-supervised fashion.Two new benchmarks, Enron-Context and Reddit-Context, are introduced for formality and offensiveness style transfer.Experimental results on these datasets demonstrate the effectiveness of the proposed CAST model over state-of-the-art methods across style accuracy, content preservation and contextual consistency metrics. 1