基于半监督知识蒸馏的客户交易分类改进
Improved Customer Transaction Classification using Semi-Supervised Knowledge Distillation
摘要：
在取货和送货服务中，基于客户提供的自由文本的交易分类是一个具有挑战性的问题。它涉及将各种各样的客户输入关联到一组固定的类别，同时适应各种客户写作风格。这种分类对企业来说很重要：它有助于了解市场需求和趋势，并有助于为不同的客户群体构建个性化的体验。因此，以高精度和召回率大规模捕获这些类别信息趋势至关重要。在本文中，我们关注一个特定的用例，其中单个类别驱动每个事务。本文提出了一种基于半监督和知识蒸馏框架的事务分类方法。该方法使用客户给出的自由文本输入来识别交易的类别。我们使用弱标记，并注意到性能增益与使用人工注释的样本相似。在大型内部数据集和20 Newsgroup数据集上，我们看到RoBERTa在分类任务中表现最好。此外，使用ALBERT模型（它的参数比RoBERTa的参数少33倍），以RoBERTa作为教师，我们看到与RoBERTa相似的性能，并且比未适应的ALBERT性能更好。这个框架，作为一个学生和ROBERTa作为教师，在本文中进一步被称为R-ALBERT。该模型已投入生产，并被企业用于了解不断变化的趋势并做出适当的决策。
英文摘要：
In pickup and delivery services, transaction classification based on customer provided free text is a challenging problem. It involves the association of a wide variety of customer inputs to a fixed set of categories while adapting to the various customer writing styles. This categorization is important for the business: it helps understand the market needs and trends, and also assist in building a personalized experience for different segments of the customers. Hence, it is vital to capture these category information trends at scale, with high precision and recall. In this paper, we focus on a specific use-case where a single category drives each transaction. We propose a cost-effective transaction classification approach based on semi-supervision and knowledge distillation frameworks. The approach identifies the category of a transaction using free text input given by the customer. We use weak labelling and notice that the performance gains are similar to that of using human-annotated samples. On a large internal dataset and on 20Newsgroup dataset, we see that RoBERTa performs the best for the categorization tasks. Further, using an ALBERT model (it has 33x fewer parameters vis-a-vis parameters of RoBERTa), with RoBERTa as the Teacher, we see a performance similar to that of RoBERTa and better performance over unadapted ALBERT. This framework, with ALBERT as a student and RoBERTa as teacher, is further referred to as R-ALBERT in this paper. The model is in production and is used by business to understand changing trends and take appropriate decisions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
