Loan Question Answering Platform Based on ERNIE and Knowledge Graph

Yuquan Fan, Xianglin Cao, Hong Xiao, Weilin Zhou, Wenchao Jiang
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Abstract

At present, the excessive amount of loan consultation has brought great pressure to manual customer service. However, the existing loan question answering (QA) platforms cannot solve this problem well because of their poor understanding ability. Therefore, the authors constructs a loan QA platform based on ERNIE and knowledge graph (KG). Firstly, they use semi-automatic methods to construct KG with data from a loan company. Secondly, they use token-level random mask strategy (TRM), word-level fixed mask strategy (WFM), and fine-tuning strategy integrating knowledge (IK) to train ERNIE. Finally, they construct a QA platform based on KG and trained ERNIE and experiment with proprietary datasets. The results show that ERNIE trained after three strategies achieve average improvements of 14.7% on judging intention similarity of sentence pairs and 14.28% on retrieving the most similar intention problem compared with the baseline. It also shows that their platform achieves an average improvement of 13% on question answering compared with the customer service app of the loan company.
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基于ERNIE和知识图谱的贷款问答平台
目前,过多的贷款咨询给人工客服带来了很大的压力。然而,现有的贷款问答平台由于理解能力差,无法很好地解决这一问题。为此,作者构建了一个基于ERNIE和知识图谱(KG)的贷款质量保证平台。首先,他们使用半自动方法使用贷款公司的数据构建KG。其次,采用令牌级随机掩码策略(TRM)、词级固定掩码策略(WFM)和整合知识的微调策略(IK)来训练ERNIE。最后,他们构建了一个基于KG和训练好的ERNIE的QA平台,并在专有数据集上进行了实验。结果表明,经过三种策略训练后的ERNIE在判断句子对意图相似度上平均提高14.7%,在检索最相似意图问题上平均提高14.28%。数据还显示,与贷款公司的客服app相比,他们的平台在问答方面平均提升了13%。
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