利用扰动一致性改进多跳知识库问答

IF 0.9 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Xin Wang, Hongbin Shi
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引用次数: 1

摘要

多跳知识库问答旨在通过知识库中的多跳关系推理来回答自然语言问题。该任务的一个重要挑战是缺乏对推理路径的标签,这导致在训练中可能通过错误的路径产生正确的答案,而在测试中不能很好地泛化。最近的研究试图通过设计奖励塑造或引入额外的信息来产生中间路径的监督信号来应对这一挑战。但它们需要额外的专家经验和标签信息。为了解决这种情况,我们提出了一种新的方法,在师生框架下,它利用扰动一致性来学习中间路径。在教师网络中,我们通过应用随机扰动构造接近的数据点来进行中间路径预测。受数据平滑假设的启发,接近数据点的标签应该是相同的,对构建数据点和原始数据点的预测的一致性损失进行了评估。通过利用从教师网络中学到的中间分布,学生网络可以更精确地回答问题。在两个基准数据集上进行了大量的实验,结果证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging Perturbation Consistency to Improve Multi-hop Knowledge Base Question Answering
Multi-hop knowledge base question answering aims to answer natural language questions through multi-hop relation reasoning in the knowledge base. An important challenge of the task is the lack of labels for reasoning paths, which leads to the possibility to produce correct answers through incorrect paths in the training, and cannot generalize well in testing. Recently research has attempted to handle the challenge by devising reward shaping or introducing additional information to generate supervision signals of intermediate paths. But they required extra expert experience and label information. To address this situation, we propose a novel method under the teacher-student framework, it leverages perturbation consistency to learn intermediate paths. In the teacher network, we construct close data points for intermediate path prediction by applying random perturbations. Inspired by the data smoothing assumption that labels of close data points should be the same, a consistency loss over predictions of constructed data points and original ones is evaluated. The student network is used to answer questions more precisely by leveraging the intermediate distribution learned from the teacher network. Extensive experiments on two benchmark datasets are conducted, and the results have demonstrated the effectiveness of the proposed method.
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来源期刊
Scalable Computing-Practice and Experience
Scalable Computing-Practice and Experience COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.00
自引率
0.00%
发文量
10
期刊介绍: The area of scalable computing has matured and reached a point where new issues and trends require a professional forum. SCPE will provide this avenue by publishing original refereed papers that address the present as well as the future of parallel and distributed computing. The journal will focus on algorithm development, implementation and execution on real-world parallel architectures, and application of parallel and distributed computing to the solution of real-life problems.
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