This paper summarizes our submission to Task 2 of the second track of the
10th Dialog System Technology Challenge (DSTC10) "Knowledge-grounded
Task-oriented Dialogue Modeling on Spoken Conversations". Similar to the
previous year's iteration, the task consists of three subtasks: detecting
whether a turn is knowledge seeking, selecting the relevant knowledge document
and finally generating a grounded response. This year, the focus lies on
adapting the system to noisy ASR transcripts. We explore different approaches
to make the models more robust to this type of input and to adapt the generated
responses to the style of spoken conversations. For the latter, we get the best
results with a noisy channel model that additionally reduces the number of
short and generic responses. Our best system achieved the 1st rank in the
automatic and the 3rd rank in the human evaluation of the challenge.

本文介绍了我们在第十届对话系统技术挑战赛第二轨知识引导的任务导向对话建模中的提交内容。在适应 ASR 转录的噪声方面探索不同的方法来使得模型更加健壮，并采用噪声通道模型来适应口语交流的风格。我们的最佳系统在挑战的自动和人工评估中分别排名第一和第三。

使用数据增强和噪声通道模型将基于文档的对话系统适应为口语对话

Adapting Document-Grounded Dialog Systems to Spoken Conversations using  Data Augmentation and a Noisy Channel Model

Direct decoding for task-oriented dialogue is known to suffer from the
explaining-away effect, manifested in models that prefer short and generic
responses. Here we argue for the use of Bayes' theorem to factorize the
dialogue task into two models, the distribution of the context given the
response, and the prior for the response itself. This approach, an
instantiation of the noisy channel model, both mitigates the explaining-away
effect and allows the principled incorporation of large pretrained models for
the response prior. We present extensive experiments showing that a noisy
channel model decodes better responses compared to direct decoding and that a
two stage pretraining strategy, employing both open-domain and task-oriented
dialogue data, improves over randomly initialized models.

使用 Bayes' 定理将任务型对话分解为两个模型，上下文给定回复的分布和回复本身的先验，采用嘈杂信道模型的方法既弱化了解释效应，又允许合理地将大型预先训练的模型纳入回复先验中。大量实验证明，相比于直接解码，嘈杂信道模型具有更好的解码性能，并且采用两阶段预训练策略（同时使用开放领域和任务导向对话数据）可以优于随机初始化的模型。