User experience (UX) is a part of human-computer interaction (HCI) research and focuses on increasing intuitiveness, transparency, simplicity, and trust for system users. Most of the UX research for machine learning (ML) or natural language processing (NLP) focuses on a data-driven methodology, i.e., it fails to focus on users' requirements, and engages domain users mainly for usability evaluation. Moreover, more typical UX methods tailor the systems towards user usability, unlike learning about the user needs first. The paper proposes a methodology for integrating generative UX research into developing domain NLP applications. Generative UX research employs domain users at the initial stages of prototype development, i.e., ideation and concept evaluation, and the last stage for evaluating the change in user value. In the case study, we report the full-cycle prototype development of a domain-specific semantic search for daily operations in the process industry. Our case study shows that involving domain experts increases their interest and trust in the final NLP application. Moreover, we show that synergetic UX+NLP research efficiently considers data- and user-driven opportunities and constraints, which can be crucial for NLP applications in narrow domains

翻译：用户体验（UX）是人机交互（HCI）研究的一部分，专注于提升系统用户的直观性、透明性、简洁性和信任度。当前针对机器学习（ML）或自然语言处理（NLP）的大多数用户体验研究采用数据驱动方法论，未能聚焦用户需求，且主要将领域用户局限于可用性评估环节。此外，传统用户体验方法更倾向于将系统调整为符合用户可用性，而非首先理解用户需求。本文提出一种将生成式用户体验研究融入领域NLP应用开发的方法论。生成式用户体验研究在原型开发的初期阶段（即创意构思与概念评估）即引入领域用户参与，并在最终阶段评估用户价值的变化。在案例研究中，我们报告了面向流程工业日常操作的领域特定语义搜索的全周期原型开发过程。案例研究表明，领域专家的参与提升了其对最终NLP应用的兴趣与信任。此外，我们证实协同式用户体验与NLP研究能有效兼顾数据驱动与用户驱动的机会与约束，这对窄领域NLP应用具有关键意义。