Machine reading comprehension aims to enable machines to answer questions related to a given article.To address the machine reading comprehension models in low-resource languages,this paper proposes an end-to-end at-tention based model for Tibetan named Ti-Reader.First,to encode more fine-grained Tibetan text information,this paper combines syllables and words for word embedding,and then uses word-level attention to capture the keywords in the article.Moreover,the re-read mechanism is applied to capture the semantic information between the article and the questions,and the self-attention is used to match the hidden variables of the question and the answer.The experimental results show that Ti-Reader improves the performance of Tibetan machine reading comprehension,while preserving a good performance on the English dataset SQuAD.
维普
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