Conversation Style Transfer using Few-Shot Learning
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Conventional text style transfer approaches for natural language focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e.g., formality). When applying style transfer on conversations such as task-oriented dialogues, existing approaches suffer from these limitations as context can play an important role and the style attributes are often difficult to define in conversations. In this paper, we introduce conversation style transfer as a few-shot learning problem, where the model learns to perform style transfer by observing only the target-style dialogue examples. We propose a novel in-context learning approach to solve the task with style-free dialogues as a pivot. Human evaluation shows that by incorporating multi-turn context, the model is able to match the target style while having better appropriateness and semantic correctness compared to utterance-level style transfer. Additionally, we show that conversation style transfer can also benefit downstream tasks. Results on multi-domain intent classification tasks show improvement in F1 scores after transferring the style of training data to match the style of test data.
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