基于MHSA和句法关系增强的机器阅读理解方法研究

机器阅读理解 (Machine reading comprehension, MRC)是自然语言处理领域中一项重要研究任务, 其目标是通过机器理解给定的阅读材料和问题, 最终实现自动答题. 目前联合观点类问题解答和答案依据挖掘的多任务联合学习研究在机器阅读理解应用中受到广泛关注, 它可以同时给出问题答案和支撑答案的相关证据, 然而现有观点类问题的答题方法在答案线索识别上表现还不是太好, 已有答案依据挖掘方法仍不能较好捕获段落中词语之间的依存关系. 基于此, 引入多头自注意力(Multi-head self-attention, MHSA)进一步挖掘阅读材料中观点类问题的文字线索, 改进了观点类问题的自动解答方法; 将句法关系融入到图构建过程中, 提出了基于关联要素关系图的多跳推理方法, 实现了答案支撑句挖掘; 通过联合优化两个子任务, 构建了基于多任务联合学习的阅读理解模型. 在2020中国“法研杯”司法人工智能挑战赛(China AI Law Challenge 2020, CAIL2020)和HotpotQA数据集上的实验结果表明, 本文提出的方法比已有基线模型的效果更好.

Research on Machine Reading Comprehension Method Based on MHSA and Syntactic Relations Enhancement

Machine reading comprehension (MRC), which aims to understand the question and the relevant article to answer questions automatically, is an important research task in natural language processing. Recently, the multi-task joint learning research combining opinion question solving and answer evidence mining has attracted much attention. Although methods proposed by such researches always provide both the answer and the relevant evidence simultaneously, neither are the existing methods handling the opinion-type questions good at identifying the clues to the answer, nor are the previous methods mining the answer evidence good at capturing the dependency relationship between words in the given paragraph. Therefore, the method to solve the opinion-type questions has been improved by further exploring the related text clues within the given reading materials through utilizing multi-head self-attention (MHSA); a multi-hop reasoning method realizing the mining of supporting sentences to the answer has been developed by integrating syntactic relation into the construction process of the element graph; a multi-task joint learning model for MRC has been constructed by optimizing the two sub-tasks jointly. Experiments on MRC datasets of CAIL2020 (China AI Law Challenge 2020) and HotpotQA show that the proposed method can provide better results than the existing baseline models.