四川大学学报（自然科学版）2024，Vol.61Issue(2)：40-48,9.DOI:10.19907/j.0490-6756.2024.022001
一种利用对抗样本提高抽取式阅读理解模型效果的方法
A method of improving the performance of extractive reading comprehension model by using adversarial samples
摘要
Abstract
Extractive machine reading comprehension is a crucial task in natural language processing,where machines are required to extract the answer(fragments in inputting text)to a given question on the base of reading and understanding natural language text,and refuse to provide any answer when the given question is unanswerable.The task becomes more challenging when faced with unanswerable questions,where machines should refrain from providing any answer,especially when an inputting text contains plausible text fragments.Existing models are easily confused such fragments as an answer to the given question,and then wrongly judge answerability of question.To further improve the effect of extractive machine reading comprehension model,this paper takes plausible answers in SQuAD 2.0 dataset as the adversarial samples,which are used as positive examples extracted from text fragments of the answer and negative examples for judging answer-ability of question.Thus,this method increases the ranking loss based on the cross-entropy loss of the an-swers in the existing models.Experiments on SQuAD 2.0 shows that the proposed method can improve ro-bustness of existing extractive machine reading comprehension models,and significantly improve the effect of answerability judgment and extraction of text fragments about answer.关键词
阅读理解/不可回答问题/对抗样本Key words
Reading comprehension/Unanswerable question/Adversarial samples分类
信息技术与安全科学引用本文复制引用
何东,于晓昕,叶子铭,于中华,陈黎..一种利用对抗样本提高抽取式阅读理解模型效果的方法[J].四川大学学报（自然科学版）,2024,61(2):40-48,9.基金项目
四川省重点研发项目(2023YFG0265) （2023YFG0265）
