论文标题
对话中的噪音来源以及如何与他们打交道
Sources of Noise in Dialogue and How to Deal with Them
论文作者
论文摘要
培训对话系统通常需要处理嘈杂的培训示例和意外的用户输入。尽管存在普遍存在，但目前缺乏对对话噪声的准确调查，也没有对每种噪声类型对任务性能的影响有明确的感觉。本文通过首先构建对话系统遇到的噪声分类法来解决这一差距。此外，我们运行了一系列实验，以显示不同模型在遇到不同水平的噪声和类型噪声时的行为。我们的结果表明，模型非常强大，可以标记现有的denoing算法通常可以解决的错误，但是这种性能遭受了特定于对话的噪声。在这些观察结果的驱动下，我们设计了一种专门用于会话设置的数据清洁算法，并将其作为概念验证，以验证目标对话。
Training dialogue systems often entails dealing with noisy training examples and unexpected user inputs. Despite their prevalence, there currently lacks an accurate survey of dialogue noise, nor is there a clear sense of the impact of each noise type on task performance. This paper addresses this gap by first constructing a taxonomy of noise encountered by dialogue systems. In addition, we run a series of experiments to show how different models behave when subjected to varying levels of noise and types of noise. Our results reveal that models are quite robust to label errors commonly tackled by existing denoising algorithms, but that performance suffers from dialogue-specific noise. Driven by these observations, we design a data cleaning algorithm specialized for conversational settings and apply it as a proof-of-concept for targeted dialogue denoising.
