论文标题
珠宝店对话聊天机器人
Jewelry Shop Conversational Chatbot
论文作者
论文摘要
自从商业领域聊天机器人出现以来，它们已被广泛在客户服务部门工作。通常，这些商业聊天机器人是基于检索的，因此它们无法响应提供的数据集中缺少的查询。相反，生成的聊天机器人尝试创建最合适的响应，但大多无法在客户机器人对话框中创建平稳的流程。由于客户在收到响应后仍有几乎没有选择的选择，因此对话框变得短缺。通过我们的工作，我们试图最大程度地提高简单的对话代理的智能，以便可以回答看不见的查询，并产生后续问题或备注。我们已经为一家珠宝店建造了一个聊天机器人，该聊天机器人通过找到与语料库模式的输入相似，从而找到了客户查询的基本目标。我们的系统为客户提供了音频输入界面，因此他们可以用自然语言对其进行交谈。将音频转换为文本后，我们训练了模型来提取查询的意图，找到适当的响应并以自然的人类声音与客户交谈。为了衡量系统的性能，我们使用了性能指标，例如召回，精度和F1分数。
Since the advent of chatbots in the commercial sector, they have been widely employed in the customer service department. Typically, these commercial chatbots are retrieval-based, so they are unable to respond to queries absent in the provided dataset. On the contrary, generative chatbots try to create the most appropriate response, but are mostly unable to create a smooth flow in the customer-bot dialog. Since the client has few options left for continuing after receiving a response, the dialog becomes short. Through our work, we try to maximize the intelligence of a simple conversational agent so it can answer unseen queries, and generate follow-up questions or remarks. We have built a chatbot for a jewelry shop that finds the underlying objective of the customer's query by finding similarity of the input to patterns in the corpus. Our system features an audio input interface for clients, so they may speak to it in natural language. After converting the audio to text, we trained the model to extract the intent of the query, to find an appropriate response and to speak to the client in a natural human voice. To gauge the system's performance, we used performance metrics such as Recall, Precision and F1 score.
