Teaching Machines to Read and Comprehend Tibetan Text

Teaching machine to comprehend a passage and answer corresponding questions, the machine reading comprehension (MRC) has attracted much attention in current years. However, most models are designed to finish English or Chinese MRC task, Considering lack of MRC dataset, the low-resource languages MRC tasks, such as Tibetan, it is hard to get high performance. To solve this problem, this paper constructs a span-style Tibetan MRC dataset named TibetanQA and proposes a hierarchical attention network model for Tibetan MRC task which includes word-level attention and re-read attention. And the experiments prove the effectiveness of our model.