论文标题
基于深度双向变压器的金融服务聊天机器人
A Financial Service Chatbot based on Deep Bidirectional Transformers
论文作者
论文摘要
我们使用深层双向变压器模型（BERT）开发聊天机器人，以处理金融投资客户服务中的客户问题。该机器人可以识别381条意图，并决定何时说“我不知道”，并向人类运营商提升无关/不确定的问题。我们的主要新颖贡献是关于伯特的不确定性度量的讨论，其中有三种不同的方法在实际问题上进行了比较。我们研究了两个不确定性指标，信息熵和辍学的差异，然后进行了混合智能编程以优化决策阈值。另一个新颖的贡献是将伯特用作自动拼写校正中的语言模型。意外拼写错误的输入可以显着降低意图分类性能。提出的方法结合了蒙版语言模型和单词编辑距离的概率，以找到拼写错误单词的最佳更正。聊天机器人和整个对话AI系统是使用开源工具开发的，并部署在我们公司的Intranet中。所提出的方法对于在其特定业务领域中寻求类似内部解决方案的行业很有用。我们共享所有代码和示例聊天机器人，构建在GitHub上的公共数据集上。
We develop a chatbot using Deep Bidirectional Transformer models (BERT) to handle client questions in financial investment customer service. The bot can recognize 381 intents, and decides when to say "I don't know" and escalates irrelevant/uncertain questions to human operators. Our main novel contribution is the discussion about uncertainty measure for BERT, where three different approaches are systematically compared on real problems. We investigated two uncertainty metrics, information entropy and variance of dropout sampling in BERT, followed by mixed-integer programming to optimize decision thresholds. Another novel contribution is the usage of BERT as a language model in automatic spelling correction. Inputs with accidental spelling errors can significantly decrease intent classification performance. The proposed approach combines probabilities from masked language model and word edit distances to find the best corrections for misspelled words. The chatbot and the entire conversational AI system are developed using open-source tools, and deployed within our company's intranet. The proposed approach can be useful for industries seeking similar in-house solutions in their specific business domains. We share all our code and a sample chatbot built on a public dataset on Github.
