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）研究的重要组成部分，其核心在于提升系统用户的直觉性、透明度、简洁性与信任感。当前针对机器学习（ML）或自然语言处理（NLP）的UX研究多采用数据驱动方法，即未能聚焦用户需求，且主要将领域用户局限于可用性评估环节。此外，传统UX方法更侧重于根据用户可用性来调整系统，而非优先了解用户需求。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UX研究在原型开发的初始阶段（即构思与概念评估）以及最终阶段（评估用户价值变化）引入领域用户参与。在案例研究中，我们报告了面向流程工业日常运营的领域特定语义搜索的完整原型开发周期。研究表明，邀请领域专家参与可提升其对最终NLP应用的兴趣与信任度。此外，我们论证了协同式UX+NLP研究能有效兼顾数据驱动与用户驱动的机遇与约束，这对窄领域NLP应用具有关键意义。