  Spoken question answering (SQA) requires fine-grained understanding of both
spoken documents and questions for the optimal answer prediction. In this
paper, we propose novel training schemes for spoken question answering with a
self-supervised training stage and a contrastive representation learning stage.
In the self-supervised stage, we propose three auxiliary self-supervised tasks,
including utterance restoration, utterance insertion, and question
discrimination, and jointly train the model to capture consistency and
coherence among speech documents without any additional data or annotations. We
then propose to learn noise-invariant utterance representations in a
contrastive objective by adopting multiple augmentation strategies, including
span deletion and span substitution. Besides, we design a Temporal-Alignment
attention to semantically align the speech-text clues in the learned common
space and benefit the SQA tasks. By this means, the training schemes can more
effectively guide the generation model to predict more proper answers.
Experimental results show that our model achieves state-of-the-art results on
three SQA benchmarks.
