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Yin, J., Zhao, W.X. and Li, X.M. (2017) Type-Aware Question Answering over Knowledge Base with Attention-Based Tree-Structured Neural Networks. Journal of Computer Science and Technology, 32, 805-813. https://doi.org/10.1007/s11390-017-1761-8

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• TITLE: Teaching Machines to Read and Comprehend Tibetan Text

  AUTHORS: Yuan Sun, Sisi Liu, Chaofan Chen, Zhengcuo Dan, Xiaobing Zhao

  KEYWORDS: Machine Reading Comprehension, Hierarchical Attention, Dataset

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.9, September 30, 2021

  ABSTRACT: 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.