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面向鲁棒性增强的多任务机器阅读理解

Robustness enhancement oriented multi task machine reading comprehension
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摘要 目前抽取式机器阅读理解已经取得了很好的成果。然而,许多研究工作表明,机器阅读理解模型在过敏感性、过稳定性等方面的鲁棒性还有待提高。为了解决该问题,提出了一种面向鲁棒性增强的多任务抽取式阅读理解模型,加强模型在篇章和问题2方面的理解能力。通过多任务学习方式,将答案抽取作为主要任务,证据句判断和问题分类作为辅助任务,实现编码器之间的信息共享。在鲁棒性测试集上的实验结果表明,所提模型对比基线模型有明显的性能提升。 At present,Machine Reading Comprehension(MRC)has achieved good success.However,many researches show that MRC models still have some problems in the robustness in terms of over-sensitivity and over-stability.In order to solve these problems,a multi-task MRC model oriented to robustness enhancement is proposed to strengthen the model’s ability to understand the passage and the problem.Specifically,in the multi-task learning method,answer extraction is the main task,and the judgment of evidence sentences and the classification of question are auxiliary tasks,which realizes information sharing between these tasks.The experimental results on the robustness test sets show that the proposed model’s performance has a significant improvement compared with the baseline models.
作者 谭红叶 行覃杰 TAN Hong-ye;XING Qin-jie(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China)
出处 《计算机工程与科学》 CSCD 北大核心 2023年第2期363-369,共7页 Computer Engineering & Science
基金 国家自然科学基金(62076155)。
关键词 鲁棒 多任务学习 机器阅读理解 robustness multi-task learning machine reading comprehension
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