@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://preview.aclanthology.org/ingest-acl/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."
}