  Building robust and general dialogue models for spoken conversations is
challenging due to the gap in distributions of spoken and written data. This
paper presents our approach to build generalized models for the
Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations
Challenge of DSTC-10. In order to mitigate the discrepancies between spoken and
written text, we mainly employ extensive data augmentation strategies on
written data, including artificial error injection and round-trip text-speech
transformation. To train robust models for spoken conversations, we improve
pre-trained language models, and apply ensemble algorithms for each sub-task.
Typically, for the detection task, we fine-tune \roberta and ELECTRA, and run
an error-fixing ensemble algorithm. For the selection task, we adopt a
two-stage framework that consists of entity tracking and knowledge ranking, and
propose a multi-task learning method to learn multi-level semantic information
by domain classification and entity selection. For the generation task, we
adopt a cross-validation data process to improve pre-trained generative
language models, followed by a consensus decoding algorithm, which can add
arbitrary features like relative \rouge metric, and tune associated feature
weights toward \bleu directly. Our approach ranks third on the objective
evaluation and second on the final official human evaluation.

Computation and Language