  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.
