论文标题
基于条件意识和修订变压器的问题回答基于条件意识和修订变压器的错误背景知识的纠正
Correction of Faulty Background Knowledge based on Condition Aware and Revise Transformer for Question Answering
论文作者
论文摘要
近年来，对问答的研究受到了越来越多的关注。这项工作着重于提供与与该问题相对应的用户意图和条件信息兼容的答案，例如电子商务中的交货状态和库存信息。但是，在现实世界中，这些条件可能是错误的或不完整的。尽管现有的问答系统已经考虑了外部信息，例如在知识库中的分类属性和三倍，但他们都认为外部信息是正确且完整的。为了减轻条件值有缺陷的效果，本文提出了条件意识和修改变压器（CAR-Transformer）。 Car-Transformer（1）根据整个对话和原始条件值修改每个条件值，并且（2）它编码修订后的条件，并利用嵌入的条件选择答案。现实世界中客户服务数据集的实验结果表明，当与问题相对应的条件存在时，CAR-Transformer仍然可以选择适当的答复，并且在自动和人类评估上大大优于基线模型。建议的CAR-Transformer可以扩展到需要考虑调节信息的其他NLP任务。
The study of question answering has received increasing attention in recent years. This work focuses on providing an answer that compatible with both user intent and conditioning information corresponding to the question, such as delivery status and stock information in e-commerce. However, these conditions may be wrong or incomplete in real-world applications. Although existing question answering systems have considered the external information, such as categorical attributes and triples in knowledge base, they all assume that the external information is correct and complete. To alleviate the effect of defective condition values, this paper proposes condition aware and revise Transformer (CAR-Transformer). CAR-Transformer (1) revises each condition value based on the whole conversation and original conditions values, and (2) it encodes the revised conditions and utilizes the conditions embedding to select an answer. Experimental results on a real-world customer service dataset demonstrate that the CAR-Transformer can still select an appropriate reply when conditions corresponding to the question exist wrong or missing values, and substantially outperforms baseline models on automatic and human evaluations. The proposed CAR-Transformer can be extended to other NLP tasks which need to consider conditioning information.
