Contextual Text Style Transfer

• URL: http://arxiv.org/abs/2005.00136v1
• Date: Thu, 30 Apr 2020 23:01:12 GMT
• Title: Contextual Text Style Transfer
• Authors: Yu Cheng, Zhe Gan, Yizhe Zhang, Oussama Elachqar, Dianqi Li, Jingjing Liu
• Abstract summary: Contextual Text Style Transfer aims to translate a sentence into a desired style with its surrounding context taken into account. We propose a Context-Aware Style Transfer (CAST) model, which uses two separate encoders for each input sentence and its surrounding context. Two new benchmarks, Enron-Context and Reddit-Context, are introduced for formality and offensiveness style transfer.
• Score: 73.66285813595616
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 with 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 self-reconstruction 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.

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