Contextual Text Style Transfer
Yu Cheng, Zhe Gan, Yizhe Zhang, Oussama Elachqar, Dianqi Li, Jingjing, Liu

TL;DR
This paper introduces Contextual Text Style Transfer, a task that considers surrounding context for style transfer, and proposes the CAST model to improve semantic preservation and contextual consistency with limited data.
Contribution
The paper presents a novel task of contextual style transfer and a new model, CAST, that effectively incorporates context and semi-supervised learning for improved style transfer.
Findings
CAST outperforms state-of-the-art methods in style accuracy.
The model maintains better content and contextual consistency.
New benchmarks Enron-Context and Reddit-Context are introduced.
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: () how to preserve the semantic meaning of target sentence and its consistency with surrounding context during transfer; () 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…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Hate Speech and Cyberbullying Detection
