  Spoken conversational question answering (SCQA) requires machines to model
complex dialogue flow given the speech utterances and text corpora. Different
from traditional text question answering (QA) tasks, SCQA involves audio signal
processing, passage comprehension, and contextual understanding. However, ASR
systems introduce unexpected noisy signals to the transcriptions, which result
in performance degradation on SCQA. To overcome the problem, we propose CADNet,
a novel contextualized attention-based distillation approach, which applies
both cross-attention and self-attention to obtain ASR-robust contextualized
embedding representations of the passage and dialogue history for performance
improvements. We also introduce the spoken conventional knowledge distillation
framework to distill the ASR-robust knowledge from the estimated probabilities
of the teacher model to the student. We conduct extensive experiments on the
Spoken-CoQA dataset and demonstrate that our approach achieves remarkable
performance in this task.
