  Reading comprehension by machine has been widely studied, but machine
comprehension of spoken content is still a less investigated problem. In this
paper, we release Open-Domain Spoken Question Answering Dataset (ODSQA) with
more than three thousand questions. To the best of our knowledge, this is the
largest real SQA dataset. On this dataset, we found that ASR errors have
catastrophic impact on SQA. To mitigate the effect of ASR errors, subword units
are involved, which brings consistent improvements over all the models. We
further found that data augmentation on text-based QA training examples can
improve SQA.
