@inproceedings{ahmad-etal-2026-unified,
title = "A Unified Ambiguity Signal for Calibrated Multimodal Emotion Recognition",
author = "Ahmad, Tahir and
Mukhtar, Raza and
Park, Chanjun and
Yoon, Yeo Chan",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the {A}sia-Pacific Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.aacl-long.111/",
pages = "2432--2453",
ISBN = "979-8-89176-439-2",
abstract = "Multimodal emotion recognition (MER) in conversation is inherently ambiguous, as linguistic, acoustic, and visual cues may support multiple plausible interpretations. Existing approaches often treat ambiguity as prediction uncertainty rather than modeling its structured sources. We propose UAF, an ambiguity-aware framework for MER built around a single unified ambiguity signal. UAF estimates a normalized per-sample Model-based Ambiguity Degree (MAD) from the predicted emotion distribution by measuring its proximity to the maximally ambiguous uniform distribution, and uses it as a shared control signal rather than a post-hoc uncertainty score. This single scalar coordinates three parts of learning: it aligns representation radius with ambiguity, emphasizes ambiguous samples during optimization, and scales OT-based perturbation consistency. The fused evidence is organized into context, speaker, temporal, cross-modal, and residual subspaces that serve as the substrate on which this signal operates. Experiments on IEMOCAP, CMU-MOSEI, and MELD show that UAF yields consistently better calibration and high-ambiguity reliability alongside competitive accuracy and weighted-F1, showing that a single ambiguity signal can be an effective shared training signal for reliable MER"
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%0 Conference Proceedings
%T A Unified Ambiguity Signal for Calibrated Multimodal Emotion Recognition
%A Ahmad, Tahir
%A Mukhtar, Raza
%A Park, Chanjun
%A Yoon, Yeo Chan
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-439-2
%F ahmad-etal-2026-unified
%X Multimodal emotion recognition (MER) in conversation is inherently ambiguous, as linguistic, acoustic, and visual cues may support multiple plausible interpretations. Existing approaches often treat ambiguity as prediction uncertainty rather than modeling its structured sources. We propose UAF, an ambiguity-aware framework for MER built around a single unified ambiguity signal. UAF estimates a normalized per-sample Model-based Ambiguity Degree (MAD) from the predicted emotion distribution by measuring its proximity to the maximally ambiguous uniform distribution, and uses it as a shared control signal rather than a post-hoc uncertainty score. This single scalar coordinates three parts of learning: it aligns representation radius with ambiguity, emphasizes ambiguous samples during optimization, and scales OT-based perturbation consistency. The fused evidence is organized into context, speaker, temporal, cross-modal, and residual subspaces that serve as the substrate on which this signal operates. Experiments on IEMOCAP, CMU-MOSEI, and MELD show that UAF yields consistently better calibration and high-ambiguity reliability alongside competitive accuracy and weighted-F1, showing that a single ambiguity signal can be an effective shared training signal for reliable MER
%U https://aclanthology.org/2026.aacl-long.111/
%P 2432-2453
Markdown (Informal)
[A Unified Ambiguity Signal for Calibrated Multimodal Emotion Recognition](https://aclanthology.org/2026.aacl-long.111/) (Ahmad et al., AACL-IJCNLP 2026)
ACL
- Tahir Ahmad, Raza Mukhtar, Chanjun Park, and Yeo Chan Yoon. 2026. A Unified Ambiguity Signal for Calibrated Multimodal Emotion Recognition. In Proceedings of the 15th International Joint Conference on Natural Language Processing and the 5th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2432–2453, Hengqin, China. Association for Computational Linguistics.