Related papers: Conversation Style Transfer using Few-Shot Learning

Conversation Style Transfer using Few-Shot Learning

URL: http://arxiv.org/abs/2302.08362v2
Date: Thu, 21 Sep 2023 23:14:02 GMT
Title: Conversation Style Transfer using Few-Shot Learning
Authors: Shamik Roy, Raphael Shu, Nikolaos Pappas, Elman Mansimov, Yi Zhang, Saab Mansour and Dan Roth
Abstract summary: In this paper, we introduce conversation style transfer as a few-shot learning problem. We propose a novel in-context learning approach to solve the task with style-free dialogues as a pivot. We show that conversation style transfer can also benefit downstream tasks.
Score: 56.43383396058639
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Conventional text style transfer approaches focus on sentence-level style transfer without considering contextual information, and the style is described with attributes (e.g., formality). When applying style transfer in 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 a few example dialogues in the target style. 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/sentence-level style transfer. Additionally, we show that conversation style transfer can also benefit downstream tasks. For example, in multi-domain intent classification tasks, the F1 scores improve after transferring the style of training data to match the style of the test data.

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