什么才是好的访谈回答？定性访谈回答质量的实证分析
What Makes a Good Response? An Empirical Analysis of Quality in Qualitative Interviews
AI 导读
研究者实施并评估了 10 种访谈回答质量指标，检验哪些指标真正能预测回答对研究结论的贡献。他们构建了包含 14 个真实研究项目、343 份访谈转录和 16,940 条参与者回答的 Qualitative Interview Corpus 数据集。结果显示，与研究问题的直接相关性是最强预测因子，而常用于评估 NLP 访谈系统的清晰度和基于 surprisal 的信息量两项指标则不具预测力。
正文 · 原文
Abstract:Qualitative interviews provide essential insights into human experiences when they elicit high-quality responses. While qualitative and NLP researchers have proposed various measures of interview quality, these measures lack validation that high-scoring responses actually contribute to the study's goals. In this work, we identify, implement, and evaluate 10 proposed measures of interview response quality to determine which are actually predictive of a response's contribution to the study findings. To conduct our analysis, we introduce the Qualitative Interview Corpus, a newly constructed dataset of 343 interview transcripts with 16,940 participant responses from 14 real research projects. We find that direct relevance to a key research question is the strongest predictor of response quality. We additionally find that two measures commonly used to evaluate NLP interview systems, clarity and surprisal-based informativeness, are not predictive of response quality. Our work provides analytic insights and grounded, scalable metrics to inform the design of qualitative studies and the evaluation of automated interview systems.
| Comments: | EMNLP 2026 (Oral), 24 pages, 14 figures |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2604.05163 [cs.CL] |
| (or arXiv:2604.05163v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.05163 arXiv-issued DOI via DataCite |
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| Related DOI: | https://doi.org/10.18653/2026.emnlp-main.51
DOI(s) linking to related resources |
Submission history
From: Jonathan Ivey [view email]
[v1]
Mon, 6 Apr 2026 20:48:34 UTC (548 KB)
[v2]
Wed, 11 Nov 2026 18:27:24 UTC (505 KB)
来源：arXiv cs.AI · arxiv.org