arXiv cs.AI· Jonathan Ivey, Anjalie Field, Ziang Xiao·· 1 天前AI 评分31
什么样的回答才算好？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
研究构建了包含 14 个真实研究项目、343 份访谈记录和 16,940 条受访者回答的 Qualitative Interview Corpus，并评估 10 种访谈回答质量指标。
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