Tibetan Question Generation Based on Key Sentence and Knowledge Graph

Question generation aims to generate questions according to the given context and answer, and it has made significant progress in both Chinese and English languages. However, research on Tibetan question generation is still in the early stages, with key challenges including the omission of crucial keywords that render questions unanswerable. Existing large-scale models do not provide robust support for low-resource languages, such as GPT or BERT. To solve the problem, this article proposes to generate Tibetan questions based on key sentences and the knowledge graph. The question generator is based on the Transformer model to better understand context and multiple sources of input information. We identify key sentences to leverage closely related information, and construct a knowledge graph to incorporate more distantly related information. The results show that the BLEU-4 reaches 43.92 on TibetanQA, surpassing existing models in Tibetan question generation and significantly improving the answerability of the generated questions.