Introduction
Recent advances in neural dialogue models (Gao et al., 2018 ; Zhang et al., 2020 ; Ni et al., 2022 ) enabled the handling of complex conversational scenarios. However, one key challenge that still remains in conversational AI is to obtain the desired conversation style. Conversations in nature are dynamic and the style requirement of utterances in a conversation depends on many factors including domain (e.g., banking vs restaurant), situation (e.g., conversation with someone angry vs depressed), the speaker demographics (e.g., senior vs youngster) among others, making style transfer of the whole conversation more challenging compared to style transfer of a single utterance.

Existing studies on Text Style Transfer (TST) focus on transferring the style at the sentence level from one known style to another (Pavlick and Tetreault, 2016 ; Rao and Tetreault, 2018 ; Niu et al., 2018 ; Wang et al., 2019 ; Briakou et al., 2021 ) by ignoring contextual information, such as the previous turns in a conversation. However, as demonstrated in Figure 1 , the context plays an important role in defining conversation style.

In this paper, we explore a novel task: few-shot learning for conversation style transfer . Here, a style transfer model is expected to convert the style of an input conversation based on a few example conversations in the target style. This is in contrast with the common methodologies in TST, where the style is assumed to be describable with known and well-defined attributes (e.g., politeness, friendliness) (Zhang et al., 2018 ; Madaan et al., 2020 ; Reif et al., 2022 ) . For conversations, defining such attributes is challenging due to the dynamic nature and domain dependency. Also, the style of a conversation may be a combination of many attributes. Examples from the TWCS dataset Axelbrooke ( 2017 ) in Table 1 show that the agent responses from Chipotle and VirginTrains services are identified to have similar politeness scores by an off-the-shelf politeness classifier Danescu-Niculescu-Mizil et al. ( 2013 ) , however, their actual styles are drastically different upon a closer look.

Our proposed few-shot conversation style transfer task addresses several key challenges. Firstly, the interpretation of style attributes of the source/target dialogues is no longer required rather the style is defined solely through a few example dialogues. Secondly, it does not require parallel data in the form of source-to-target pairs, which is expensive and difficult to collect. Finally, conversation style transfer is performed with only a few example dialogues in the target style. In this paper, we show that transferring the conversation style in such a setting helps downstream applications such as chatbot personalization and domain adaptation for training.

Tailored for the proposed few-shot learning problem, we propose a novel method based on in-context learning Brown et al. ( 2020 ) . We propose to perform source-to-target style transfer with style-free dialogues as pivots. In this approach, we first prompt pre-trained large language models (LLMs) to perform style reduction on source dialogue, then use another set of prompts to rewrite the style-free dialogue to match the target style (Figure 2 ).

To accurately and efficiently evaluate the quality of conversation style transfer using different models, we conduct human evaluation on style strength, appropriateness, and semantic correctness. The appropriateness assessment is unique to conversation style transfer, which evaluates whether the transferred utterances are out-of-context. Appropriateness is critical for Task-Oriented Dialogue (TOD) applications as inappropriate responses (as shown in Figure 1 ) can result in degraded user experience. As supplementary metrics, we report automatic scores on classifier-based style strength and semantic similarity. We observe that utterance-level style transfer without contextual information can achieve the highest style strength scores, however, results in low appropriateness and low semantic correctness. On the other hand, by including contextual information, although, with lower style strength, the transferred utterances are more appropriate and semantically correct.

Conversation style transfer can be applied in downstream tasks as a data augmentation or domain adaptation technique. We perform an extrinsic evaluation of style transfer in such a setting for intent classification task, where the training and test data for the task are from different style domains. We apply few-shot conversation style transfer on the training data to convert it to the test style before training. As a result, we observe improvement in intent classification F1 scores across three domains, demonstrating the usefulness of style transfer of conversations in such downstream applications.