论文标题
通过对话助理多功能智能过程自动化
Multipurpose Intelligent Process Automation via Conversational Assistant
论文作者
论文摘要
智能过程自动化（IPA）是一种新兴技术，其主要目标是通过照顾重复性，常规和低认知任务来帮助知识工作者。可以在自然语言中与用户互动的会话代理是IPA系统的潜在应用。这样的智能代理可以通过回答特定问题并执行通常以自然语言执行的常规任务（即客户支持）来为用户提供帮助。在这项工作中，我们应对在实际工业环境中实施IPA对话助手的挑战，并且缺乏结构化培训数据。我们提出的系统带来了两个重要的好处：首先，它减少了重复性且耗时的活动，因此使工人能够专注于更聪明的过程。其次，通过与用户互动，它可以通过结构化并在某种程度上标记为培训数据来增强资源。我们通过通过转移学习（TL）方法重新实现系统的几个组件来展示后者的用法。
Intelligent Process Automation (IPA) is an emerging technology with a primary goal to assist the knowledge worker by taking care of repetitive, routine and low-cognitive tasks. Conversational agents that can interact with users in a natural language are potential application for IPA systems. Such intelligent agents can assist the user by answering specific questions and executing routine tasks that are ordinarily performed in a natural language (i.e., customer support). In this work, we tackle a challenge of implementing an IPA conversational assistant in a real-world industrial setting with a lack of structured training data. Our proposed system brings two significant benefits: First, it reduces repetitive and time-consuming activities and, therefore, allows workers to focus on more intelligent processes. Second, by interacting with users, it augments the resources with structured and to some extent labeled training data. We showcase the usage of the latter by re-implementing several components of our system with Transfer Learning (TL) methods.
