论文标题
原型与风格：在检索记忆中使用样式的编辑对话生成
Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory
论文作者
论文摘要
对话系统在对话期间表达预定语言样式的能力对其可用性和用户满意度具有直接的积极影响。我们引入了一个新的原型（PS）框架，以应对风格对话的挑战。该框架使用信息检索（IR）系统，并从检索到的响应中提取响应原型。然后，风格响应生成器将原型和所需的语言样式作为模型输入，以获得高质量和风格的响应。为了有效地训练所提出的模型，我们提出了一个新的风格感知学习目标以及一个降低噪音的学习策略。来自两种语言的三个基准数据集的结果表明，所提出的方法在内域和跨域评估中都显着优于现有基准
The ability of a dialog system to express prespecified language style during conversations has a direct, positive impact on its usability and on user satisfaction. We introduce a new prototype-to-style (PS) framework to tackle the challenge of stylistic dialogue generation. The framework uses an Information Retrieval (IR) system and extracts a response prototype from the retrieved response. A stylistic response generator then takes the prototype and the desired language style as model input to obtain a high-quality and stylistic response. To effectively train the proposed model, we propose a new style-aware learning objective as well as a de-noising learning strategy. Results on three benchmark datasets from two languages demonstrate that the proposed approach significantly outperforms existing baselines in both in-domain and cross-domain evaluations
