论文标题
使用大型的语言模型从产品规格中回答用户查询
Using Large Pretrained Language Models for Answering User Queries from Product Specifications
论文作者
论文摘要
从电子商务网站购买产品时，客户通常会有很多问题。从电子商务服务提供商和客户的角度来看，必须有一个有效的问答系统，以立即为用户查询提供答案。虽然只有在使用产品后才能回答某些问题，但是从产品规范本身可以回答许多问题。我们的工作通过找出相关的产品规格来朝着这个方向迈出第一步，这可以帮助回答用户问题。我们提出了一种自动为此问题创建培训数据集的方法。我们将最近提出的XLNET和BERT体系结构用于此问题，并发现它们提供的性能比以前用于此问题的暹罗模型更好。即使在一个垂直方面进行训练并在不同的垂直方向进行了测试，我们的模型即使进行了良好的性能。
While buying a product from the e-commerce websites, customers generally have a plethora of questions. From the perspective of both the e-commerce service provider as well as the customers, there must be an effective question answering system to provide immediate answers to the user queries. While certain questions can only be answered after using the product, there are many questions which can be answered from the product specification itself. Our work takes a first step in this direction by finding out the relevant product specifications, that can help answering the user questions. We propose an approach to automatically create a training dataset for this problem. We utilize recently proposed XLNet and BERT architectures for this problem and find that they provide much better performance than the Siamese model, previously applied for this problem. Our model gives a good performance even when trained on one vertical and tested across different verticals.
