@inproceedings{zhao-etal-2025-drfrattn,
    title = "{D}r{F}rattn: Directly Learn Adaptive Policy from Attention for Simultaneous Machine Translation",
    author = "Zhao, Libo  and
      Li, Jing  and
      Zeng, Ziqian",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.1767/",
    pages = "34881--34894",
    ISBN = "979-8-89176-332-6",
    abstract = "Simultaneous machine translation (SiMT) necessitates a robust read/write (R/W) policy to determine the optimal moments for translation, thereby balancing translation quality and latency. Effective timing in translation can align source and target tokens accurately. The attention mechanism within translation models inherently provides valuable alignment information. Building on this, previous research has attempted to modify the attention mechanism{'}s structure to leverage its alignment properties during training, employing multi-task learning to derive the read/write policy. However, this multi-task learning approach may compromise the efficacy of the attention mechanism itself. This raises a natural question: why not directly learn the read/write policy from the well-trained attention mechanism? In this study, we propose DrFrattn, a method that directly learns adaptive policies from the attention mechanism. Experimental results across various benchmarks demonstrate that our approach achieves an improved balance between translation accuracy and latency."
}