@inproceedings{huang-etal-2026-jx4mei,
title = "{JX}4{MEI}: Multimodal Semantically-Enhanced {LLM} for Joint Multimodal Emotion-Intent Explanation and Classification",
author = "Huang, YiJie and
Yang, Xiaocui and
Feng, Shi and
Wang, Daling and
Zhang, Yifei and
Yuan, Ning and
Jia, Zhuoyue and
Zhang, Wen",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1012/",
pages = "20242--20261",
ISBN = "979-8-89176-395-1",
abstract = "Existing multimodal emotion and intent recognition tasks predominantly focus on classification, overlooking the underlying rationale and intrinsic connections between these states. Bridging this gap, we propose **Joint Multimodal Emotion-Intent Explanation and Classification, JX4MEI**, a novel task requiring the model to jointly predict emotion and intent, while generating natural language explanations for why they co-occur. To support this, we present **XMEI-dataset**, a large-scale benchmark of 15,461 multimodal samples spanning 7 emotion and 9 intent categories across text, audio, and visual modalities. Unlike prior works, our dataset provides fine-grained rationales for emotion, intent, and their causal interplay, curated via a rigorous pipeline involving Chain-of-Thought generation and strict human refinement to eliminate model artifacts. Furthermore, we propose **XMEI-Qwen**, a model equipped with a novel **Language-Query Former (LQ-Former)**. By leveraging modality-specific captions as semantic queries, LQ-Former injects explicit semantic guidance into feature alignment, significantly enhancing reasoning capabilities. Empirical experiments demonstrate that XMEI-Qwen sets a new state-of-the-art on the JX4MEI task, outperforming competitive baselines in both prediction and explanation generation. Code: https://github.com/OrangeYeah1027/JX4MEI."
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<abstract>Existing multimodal emotion and intent recognition tasks predominantly focus on classification, overlooking the underlying rationale and intrinsic connections between these states. Bridging this gap, we propose **Joint Multimodal Emotion-Intent Explanation and Classification, JX4MEI**, a novel task requiring the model to jointly predict emotion and intent, while generating natural language explanations for why they co-occur. To support this, we present **XMEI-dataset**, a large-scale benchmark of 15,461 multimodal samples spanning 7 emotion and 9 intent categories across text, audio, and visual modalities. Unlike prior works, our dataset provides fine-grained rationales for emotion, intent, and their causal interplay, curated via a rigorous pipeline involving Chain-of-Thought generation and strict human refinement to eliminate model artifacts. Furthermore, we propose **XMEI-Qwen**, a model equipped with a novel **Language-Query Former (LQ-Former)**. By leveraging modality-specific captions as semantic queries, LQ-Former injects explicit semantic guidance into feature alignment, significantly enhancing reasoning capabilities. Empirical experiments demonstrate that XMEI-Qwen sets a new state-of-the-art on the JX4MEI task, outperforming competitive baselines in both prediction and explanation generation. Code: https://github.com/OrangeYeah1027/JX4MEI.</abstract>
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%0 Conference Proceedings
%T JX4MEI: Multimodal Semantically-Enhanced LLM for Joint Multimodal Emotion-Intent Explanation and Classification
%A Huang, YiJie
%A Yang, Xiaocui
%A Feng, Shi
%A Wang, Daling
%A Zhang, Yifei
%A Yuan, Ning
%A Jia, Zhuoyue
%A Zhang, Wen
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F huang-etal-2026-jx4mei
%X Existing multimodal emotion and intent recognition tasks predominantly focus on classification, overlooking the underlying rationale and intrinsic connections between these states. Bridging this gap, we propose **Joint Multimodal Emotion-Intent Explanation and Classification, JX4MEI**, a novel task requiring the model to jointly predict emotion and intent, while generating natural language explanations for why they co-occur. To support this, we present **XMEI-dataset**, a large-scale benchmark of 15,461 multimodal samples spanning 7 emotion and 9 intent categories across text, audio, and visual modalities. Unlike prior works, our dataset provides fine-grained rationales for emotion, intent, and their causal interplay, curated via a rigorous pipeline involving Chain-of-Thought generation and strict human refinement to eliminate model artifacts. Furthermore, we propose **XMEI-Qwen**, a model equipped with a novel **Language-Query Former (LQ-Former)**. By leveraging modality-specific captions as semantic queries, LQ-Former injects explicit semantic guidance into feature alignment, significantly enhancing reasoning capabilities. Empirical experiments demonstrate that XMEI-Qwen sets a new state-of-the-art on the JX4MEI task, outperforming competitive baselines in both prediction and explanation generation. Code: https://github.com/OrangeYeah1027/JX4MEI.
%U https://aclanthology.org/2026.findings-acl.1012/
%P 20242-20261
Markdown (Informal)
[JX4MEI: Multimodal Semantically-Enhanced LLM for Joint Multimodal Emotion-Intent Explanation and Classification](https://aclanthology.org/2026.findings-acl.1012/) (Huang et al., Findings 2026)
ACL
- YiJie Huang, Xiaocui Yang, Shi Feng, Daling Wang, Yifei Zhang, Ning Yuan, Zhuoyue Jia, and Wen Zhang. 2026. JX4MEI: Multimodal Semantically-Enhanced LLM for Joint Multimodal Emotion-Intent Explanation and Classification. In Findings of the Association for Computational Linguistics: ACL 2026, pages 20242–20261, San Diego, California, United States. Association for Computational Linguistics.