Zhengcuo Dan
2022
Question Generation Based on Grammar Knowledge and Fine-grained Classification
Yuan Sun
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Sisi Liu
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Zhengcuo Dan
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Xiaobing Zhao
Proceedings of the 29th International Conference on Computational Linguistics
Question generation is the task of automatically generating questions based on given context and answers, and there are problems that the types of questions and answers do not match. In minority languages such as Tibetan, since the grammar rules are complex and the training data is small, the related research on question generation is still in its infancy. To solve the above problems, this paper constructs a question type classifier and a question generator. We perform fine-grained division of question types and integrate grammatical knowledge into question type classifiers to improve the accuracy of question types. Then, the types predicted by the question type classifier are fed into the question generator. Our model improves the accuracy of interrogative words in generated questions, and the BLEU-4 on SQuAD reaches 17.52, the BLEU-4 on HotpotQA reaches 19.31, the BLEU-4 on TibetanQA reaches 25.58.
2021
面向机器阅读理解的高质量藏语数据集构建(Construction of High-quality Tibetan Dataset for Machine Reading Comprehension)
Yuan Sun (孙媛)
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Sisi Liu (刘思思)
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Chaofan Chen (陈超凡)
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Zhengcuo Dan (旦正错)
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Xiaobing Zhao (赵小兵)
Proceedings of the 20th Chinese National Conference on Computational Linguistics
机器阅读理解是通过算法让机器根据给定的上下文回答问题,从而测试机器理解自然语言的程度。其中,数据集的构建是机器阅读理解的主要任务。目前,相关算法模型在大多数流行的英语数据集上都取得了显著的成绩,甚至超过了人类的表现。但对于低资源语言,由于缺乏相应的数据集,机器阅读理解研究还处于起步阶段。本文以藏语为例,人工构建了藏语机器阅读理解数据集(TibetanQA),其中包含20000个问题答案对和1513篇文章。本数据集的文章均来自云藏网,涵盖了自然、文化和教育等12个领域的知识,问题形式多样且具有一定的难度。另外,该数据集在文章收集、问题构建、答案验证、回答多样性和推理能力等方面,均采用严格的流程以确保数据的质量,同时采用基于语言特征消融输入的验证方法说明了数据集的质量。最后,本文初步探索了三种经典的英语阅读理解模型在TibetanQA数据集上的表现,其结果难以媲美人类,这表明在藏语机器阅读理解任务上还需要更进一步的探索。
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