@inproceedings{luo-etal-2026-htmr,
    title = "{HTMR}: Hybrid Token Masking Reinforcement Learning with Verifiable Rewards for Event Argument Extraction with Multi-Perspective Reasoning",
    author = "Luo, Jianwen  and
      Jin, Yongkang  and
      Hong, Yu  and
      Yao, Jianmin",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.910/",
    pages = "19853--19873",
    ISBN = "979-8-89176-390-6",
    abstract = "Event Argument Extraction (EAE) aims to identify event arguments and assign semantic roles under a predefined schema. Recent work formulates EAE with large language models as a structured conditional generation task and applies Reinforcement Learning with Verifiable Rewards (RLVR) to optimize sequence-level event structures. However, RLVR-based EAE supervision is coarse-grained, as a single reward is assigned to the whole event structure, while optimization happens at the token level. This misalignment causes the same reward to be applied to all tokens, including those not related to event roles or arguments, introducing noise into the gradient updates and weakening the signals for decisions critical to argument extraction. To mitigate this misalignment, we propose Hybrid Token Masking RLVR (HTMR), which selectively updates policy gradients on both high-entropy forking tokens and event-critical tokens that define event structure, along with multi-perspective reasoning. Experiments across multiple benchmarks and models show that HTMR consistently outperforms full-token and high-entropy only RLVR methods. Moreover, HTMR transfers effectively as a plug-and-play approach to other tasks such as named entity recognition and relation classification. The code is publicly available for reproducibility."
}