@inproceedings{gong-hu-2025-extracting,
    title = "Extracting Military Event Temporal Relations via Relative Event Time Prediction and Virtual Adversarial Training",
    author = "Gong, Jie  and
      Hu, Qiwang",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.182/",
    pages = "3305--3317",
    ISBN = "979-8-89176-195-7",
    abstract = "Extracting temporal relationships between events in the text is crucial for understanding how events unfold over time, especially in the information-dense and precision-demanding military field. Existing models for extracting event temporal relations typically compare the relative times of events directly, neglecting the contextual information between event pairs. This can lead to difficulties in handling uncertain temporal boundaries expressed in text. In this paper, we propose an event temporal relationship extraction model for the military field, based on relative event time prediction and virtual adversarial training, MFRV. The relative event time prediction as an auxiliary task enhances the model{'}s ability to capture and infer temporal relationships. Virtual adversarial training increases the model{'}s generalization by generating adversarial samples. Additionally, we adopt the MoCo (Multi-objective gradient correction) method to balance the losses from relative event time prediction and virtual adversarial training, effectively resolving the gradient bias issue in multi-objective optimization. Furthermore, we have constructed a new dataset, TRMF, specifically for event temporal relationship extraction in the military field. Experiments conducted on TRMF, as well as widely used public datasets MATRES and TCR, demonstrate the effectiveness of MFRV."
}