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

Task-oriented Document-Grounded Dialog Systems by HLTPR@RWTH for DSTC9 and DSTC10