  In spoken conversational question answering (SCQA), the answer to the
corresponding question is generated by retrieving and then analyzing a fixed
spoken document, including multi-part conversations. Most SCQA systems have
considered only retrieving information from ordered utterances. However, the
sequential order of dialogue is important to build a robust spoken
conversational question answering system, and the changes of utterances order
may severely result in low-quality and incoherent corpora. To this end, we
introduce a self-supervised learning approach, including incoherence
discrimination, insertion detection, and question prediction, to explicitly
capture the coreference resolution and dialogue coherence among spoken
documents. Specifically, we design a joint learning framework where the
auxiliary self-supervised tasks can enable the pre-trained SCQA systems towards
more coherent and meaningful spoken dialogue learning. We also utilize the
proposed self-supervised learning tasks to capture intra-sentence coherence.
Experimental results demonstrate that our proposed method provides more
coherent, meaningful, and appropriate responses, yielding superior performance
gains compared to the original pre-trained language models. Our method achieves
state-of-the-art results on the Spoken-CoQA dataset.
