arXiv cs.CL· Bin Wen, Dai-Qiao Zhang, Tien-Ping Tan·· 11 小时前AI 评分22
对话中的选择性风险受控情绪预测：从持续性壁垒到带 Conformal 保证的偏离检测
Selective, Risk-Controlled Emotion Forecasting in Conversation: From a Persistence Barrier to Departure Detection with Conformal Guarantees
AI 导读
研究指出对话情绪预测中“预测下一情绪状态数值”是难以实现的目标，在 valence-arousal-dominance 轨迹上，重复说话者自身前一状态构成的持续性基线无法被冻结编码器修正有效超越。
正文 · 原文
Abstract:Forecasting emotion in conversation is increasingly framed as predicting the next emotional state's value. We show this targets a hard quantity in a state-observed testbed and propose a learnable, deployable reformulation instead. On valence-arousal-dominance trajectories from a dyadic corpus, repeating a speaker's own previous state sets a persistence baseline that no frozen-encoder correction meaningfully improves upon and a state-space filter exceeds; the floor requires the historical state to be observed, since an estimated anchor collapses persistence below a direct forecaster. What survives is the timing of a departure from persistence, learnable on three of four corpora and near chance on the highest-base-rate corpus, a negative result we report. We recast the task as selective departure forecasting: a lead-time-weighted metric with a leakage protocol, and commitment policies whose best choice switches with the departure rate. A dialogue-level conformal risk control bounds the expected loss of committed predictions, meeting the target in all twelve configurations tested, though the guarantee is bought through abstention, with coverage and alarm recall falling sharply on the hardest corpus. One tested language model's confidence fails as a ranking signal where another succeeds, a model-dependent failure not yet attributable to family versus scale. Code and result logs are available from the corresponding author upon request.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.05896 [cs.CL] |
| (or arXiv:2611.05896v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05896 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bin Wen [view email]
[v1]
Wed, 4 Nov 2026 03:39:22 UTC (233 KB)
来源：arXiv cs.CL · arxiv.org