Easy and effective! Data augmentation for knowledge-aware dialogue generation via multi-perspective sentences interaction

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Sisi Peng , Dan Qu , Wenlin Zhang , Hao Zhang , Shunhang Li , Minchen Xu
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引用次数: 0

Abstract

In recent years, knowledge-based dialogue generation has garnered significant attention due to its capacity to produce informative and coherent responses through the integration of external knowledge into models. However, obtaining high-quality knowledge that aligns with the dialogue content poses a considerable challenge, necessitating substantial time and resources. To tackle the issue of limited dialogue data, a majority of research endeavors concentrate on data augmentation to augment the volume of training data. Regrettably, these methods overlook knowledge augmentation, leading to a restricted diversity in input data and yielding enhancements solely in specific metrics. Real-world conversations exhibit a spectrum of characteristics, including repetitions, reversals, and interruptions, demanding a heightened level of data diversity. In this study, we introduce a straightforward yet effective data augmentation technique known as Multi-perspective Sentence Interaction to bolster the connections among sentences from varied viewpoints. Through an examination of target responses from multiple dialogue perspectives, we enhance our comprehension of the relationships between dialogue sentences, thereby facilitating the expansion of knowledge-based dialogue data. Through experiments conducted on various knowledge-based dialogue datasets and utilizing different models, our findings illustrate a notable enhancement in the quality of model generation facilitated by our method. Specifically, we observed a 3.5% enhancement in reply accuracy and a 0.1506 increase in diversity (DIST-2). Moreover, there was a substantial improvement in knowledge selection accuracy by 19.04% and a reduction in model perplexity by 31.48%.
简单有效!通过多视角句子交互为知识感知对话生成进行数据扩增
近年来,基于知识的对话生成因其能够通过将外部知识整合到模型中产生信息丰富且连贯的回应而备受关注。然而,获取与对话内容相一致的高质量知识是一项相当大的挑战,需要大量的时间和资源。为了解决对话数据有限的问题,大多数研究工作都集中在数据扩增方面,以增加训练数据量。遗憾的是,这些方法忽略了知识扩增,导致输入数据的多样性受到限制,只能在特定指标上有所提高。现实世界中的对话表现出多种特征,包括重复、颠倒和中断,这就要求数据的多样性达到更高的水平。在本研究中,我们引入了一种简单而有效的数据增强技术--多视角句子交互技术,以加强不同视角句子之间的联系。通过从多个对话视角审视目标回应,我们可以增强对对话句子之间关系的理解,从而促进基于知识的对话数据的扩展。通过在各种基于知识的对话数据集上利用不同模型进行实验,我们的研究结果表明,我们的方法显著提高了模型生成的质量。具体来说,我们观察到回复准确率提高了 3.5%,多样性(DIST-2)提高了 0.1506。此外,知识选择准确率大幅提高了 19.04%,模型复杂度降低了 31.48%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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