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While end-to-end models for spoken language understanding tasks have been explored recently, there is still no end-to-end model for spoken question answering (SQA) tasks, which would be catastrophically influenced by speech recognition errors. Meanwhile, pre-trained language models, such as BERT, have performed successfully in text question answering. To bring this advantage of pre-trained language models into spoken question answering, we propose SpeechBERT, a cross-modal transformer-based pre-trained language model. As the first exploration in end-to-end SQA models, our results matched the performance of conventional approaches that fed with output text from ASR and only slightly fell behind pre-trained language models, showing the potential of end-to-end SQA models.

Because it is not feasible to collect training data for every language, there is a growing interest in cross-lingual transfer learning. In this paper, we systematically explore zeroshot cross-lingual transfer learning on reading comprehension tasks with a language representation model pre-trained on multi-lingual corpus. The experimental results show that with pre-trained language representation zeroshot learning is feasible, and translating the source data into the target language is not necessary and even degrades the performance. We further explore what does the model learn in zero-shot setting 0 .

Reading comprehension has been widely studied. One of the most representative reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine is already comparable with human. On the other hand, accessing large collections of multimedia or spoken content is much more difficult and time-consuming than plain text content for humans. It's therefore highly attractive to develop machines which can automatically understand spoken content. In this paper, we propose a new listening comprehension task -Spoken SQuAD. On the new task, we found that speech recognition errors have catastrophic impact on machine comprehension, and several approaches are proposed to mitigate the impact.

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