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.

本文提出了一种基于对话样式迁移的 few-shot 学习模型，采用上下文学习的方式解决了对话风格转移中传统方法受限的问题，并且在多轮对话中的人工评估中，模型的话语匹配目标风格且语义正确性和合适性方面表现更好。此外，实验还表明，该模型可用于下游任务 —— 在将训练集的风格转移到测试集的情况下，多领域目标分类任务的 F1 得分有所提高。

Few-Shot 学习下的对话风格迁移

Conversation Style Transfer using Few-Shot Learning

Retrieval-based conversational systems learn to rank response candidates for
a given dialogue context by computing the similarity between their vector
representations. However, training on a single textual form of the multi-turn
context limits the ability of a model to learn representations that generalize
to natural perturbations seen during inference. In this paper we propose a
framework that incorporates augmented versions of a dialogue context into the
learning objective. We utilize contrastive learning as an auxiliary objective
to learn robust dialogue context representations that are invariant to
perturbations injected through the augmentation method. We experiment with four
benchmark dialogue datasets and demonstrate that our framework combines well
with existing augmentation methods and can significantly improve over baseline
BERT-based ranking architectures. Furthermore, we propose a novel data
augmentation method, ConMix, that adds token level perturbations through
stochastic mixing of tokens from other contexts in the batch. We show that our
proposed augmentation method outperforms previous data augmentation approaches,
and provides dialogue representations that are more robust to common
perturbations seen during inference.

本文提出了一种框架，该框架将增强版本的对话上下文纳入学习目标，并利用对比学习作为辅助目标，学习鲁棒的上下文表示，以应对推断期间出现的自然扰动。实验证明，该框架与现有的增强方法相结合，可以显著改善基于 BERT 的排名体系结构，并提供更具鲁棒性的对话表示。此外，我们提出了一种新的数据增强方法 ConMix，该方法通过从批处理中的其他上下文中随机混合 token，为对话添加 token 级扰动，优于以前的数据增强方法。

DialAug：对比学习中混合对话上下文以实现鲁棒会话建模

DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust Conversational Modeling

The noetic end-to-end response selection challenge as one track in Dialog
System Technology Challenges 7 (DSTC7) aims to push the state of the art of
utterance classification for real world goal-oriented dialog systems, for which
participants need to select the correct next utterances from a set of
candidates for the multi-turn context. This paper describes our systems that
are ranked the top on both datasets under this challenge, one focused and small
(Advising) and the other more diverse and large (Ubuntu). Previous
state-of-the-art models use hierarchy-based (utterance-level and token-level)
neural networks to explicitly model the interactions among different turns'
utterances for context modeling. In this paper, we investigate a sequential
matching model based only on chain sequence for multi-turn response selection.
Our results demonstrate that the potentials of sequential matching approaches
have not yet been fully exploited in the past for multi-turn response
selection. In addition to ranking the top in the challenge, the proposed model
outperforms all previous models, including state-of-the-art hierarchy-based
models, and achieves new state-of-the-art performances on two large-scale
public multi-turn response selection benchmark datasets.

本文提出了一种基于序列匹配模型的多轮对话响应选择算法，相比之前的模型，该模型在提升多轮对话系统中的响应选择效果方面取得了更好的表现，并在两个大规模公开数据集上均获得了最优表现。