This paper summarizes our contributions to the document-grounded dialog tasks at the 9th and 10th Dialog System Technology Challenges (DSTC9 and DSTC10). In both iterations the task consists of three subtasks: first detect whether the current turn is knowledge seeking, second select a relevant knowledge document, and third generate a response grounded on the selected document. For DSTC9 we proposed different approaches to make the selection task more efficient. The best method, Hierarchical Selection, actually improves the results compared to the original baseline and gives a speedup of 24x. In the DSTC10 iteration of the task, the challenge was to adapt systems trained on written dialogs to perform well on noisy automatic speech recognition transcripts. Therefore, we proposed data augmentation techniques to increase the robustness of the models as well as methods to adapt the style of generated responses to fit well into the proceeding dialog. Additionally, we proposed a noisy channel model that allows for increasing the factuality of the generated responses. In addition to summarizing our previous contributions, in this work, we also report on a few small improvements and reconsider the automatic evaluation metrics for the generation task which have shown a low correlation to human judgments.

翻译：本文总结了我们在第9届和第10届对话系统技术挑战赛（DSTC9和DSTC10）的基于文档的对话任务中的贡献。在两个任务中，任务分为三个部分：首先检测当前对话是否是知识获取型，其次选择相关的知识文档，最后生成基于所选文档的回复。对于DSTC9，我们提出了不同的方法来使选择任务更高效。最好的方法是层次选择，实际上比原始基准结果要好，并且速度提高了24倍。对于DSTC10中的任务，挑战是使经过书面对话训练的系统在嘈杂的自动语音识别转录上表现良好。因此，我们提出了数据增强技术来增加模型的鲁棒性，以及将生成的回复样式适应于前进行的对话的方法。此外，我们还提出了噪声信道模型，使生成的回复更加准确。除了总结我们之前的贡献之外，在本文中，我们还报告了一些小的改进，并重新考虑了生成任务的自动评估指标，这些指标与人类判断的相关性较低。