对比产业与学术界对可解释NLP的实践看法，揭示当前方法效果差、评价难的痛点。
Adoption of Explainable Natural Language Processing: Perspectives from Industry and Academia on Practices and Challenges
- 通过访谈收集从业者和研究者对可解释NLP的使用经验
- 发现当前方法满意度低，存在概念分歧与评估困难
- 呼吁建立以用户为中心的清晰框架推动实际应用
近年来，可解释自然语言处理（Explainable NLP）发展迅速。复杂模型日益透明度不足，亟需对其决策过程提供解释，这对理解其推理机制、促进部署尤为重要，尤其在高风险场景中。尽管可解释NLP备受关注，但从业者在实际采纳中的体验与有效性仍缺乏系统研究。本文通过面向产业界从业者的定性访谈，并辅以学术研究者的补充访谈，系统分析并比较双方在采用可解释方法时的动机、技术选择、满意度及现实挑战。研究发现，当前存在概念分歧，从业者对现有方法普遍不满意，且面临评估难题。结果强调需建立清晰定义与以用户为中心的框架，以推动可解释NLP在实践中更有效落地。
原文摘要 · Abstract (English)
The field of explainable natural language processing (NLP) has grown rapidly in recent years. The growing opacity of complex models calls for transparency and explanations of their decisions, which is crucial to understand their reasoning and facilitate deployment, especially in high-stakes environments. Despite increasing attention given to explainable NLP, practitioners' perspectives regarding its practical adoption and effectiveness remain underexplored. This paper addresses this research gap by investigating practitioners' experiences with explainability methods, specifically focusing on their motivations for adopting such methods, the techniques employed, satisfaction levels, and the practical challenges encountered in real-world NLP applications. Through a qualitative interview-based study with industry practitioners and complementary interviews with academic researchers, we systematically analyze and compare their perspectives. Our findings reveal conceptual gaps, low satisfaction with current explainability methods, and highlight evaluation challenges. Our findings emphasize the need for clear definitions and user-centric frameworks for better adoption of explainable NLP in practice.
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