arXiv cs.CL· Bin Wen, Dai-Qiao Zhang, Tien-Ping Tan·· 11 小时前AI 评分20
对话情绪转变预测的信息匹配审计：一种防泄漏评估协议
Information-Matched Auditing of Conversational Emotion-Shift Forecasting: A Leakage-Safe Evaluation Protocol
AI 导读
研究提出一种信息匹配、防泄漏的审计协议，通过显式承诺点、可执行因果性测试、等量四配置验证搜索和种子对话感知推理，消除对话情绪转变预测比较中的偏差。
正文 · 原文
Abstract:Comparisons in conversational emotion-shift forecasting are easily distorted when a learned model receives rich features but its transition baseline is given a noisy or unavailable current-emotion label, when hyperparameter budgets differ, or when target-turn information enters the representation. We present an information-matched, leakage-safe auditing protocol that closes these gaps with an explicit commitment point, executable causality tests, equal four-configuration validation searches, and seed-dialogue-aware inference. Applying it to six causal model families on four text corpora and two feature-source sensitivity configurations shows that a deployable train-only current-emotion classifier feeding the transition baseline yields a different verdict than an oracle diagnostic where both sides receive gold current emotion. Under the deployable condition, learned models improve over the predicted-label baseline on all four primary corpora; DailyDialog gains are large and survive a 72-comparison Holm correction (best ROC-AUC gain = +0.167, adjusted p=.0144). Under matched gold information, the baseline remains competitive on IEMOCAP and stronger on MELD, while every model retains a large DailyDialog advantage. We additionally report precision-recall AUC, calibration, a strictly utterance-local feature control, same-person self-shift forecasting, and a future-access dose-response test. An eight-item leakage checklist accompanies the protocol. Code, raw seed predictions, and reporting artifacts are available from the corresponding author upon request.
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2611.05895 [cs.CL] |
| (or arXiv:2611.05895v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05895 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bin Wen [view email]
[v1]
Wed, 4 Nov 2026 03:36:17 UTC (125 KB)
来源：arXiv cs.CL · arxiv.org