@inproceedings{tan-etal-2025-streaming,
    title = "Streaming Sequence Transduction through Dynamic Compression",
    author = "Tan, Weiting  and
      Chen, Yunmo  and
      Chen, Tongfei  and
      Qin, Guanghui  and
      Xu, Haoran  and
      Zhang, Chenyu  and
      Van Durme, Benjamin  and
      Koehn, Philipp",
    editor = "Salesky, Elizabeth  and
      Federico, Marcello  and
      Anastasopoulos, Antonis",
    booktitle = "Proceedings of the 22nd International Conference on Spoken Language Translation (IWSLT 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.iwslt-1.1/",
    pages = "1--18",
    ISBN = "979-8-89176-272-5",
    abstract = "We introduce STAR (Stream Transduction with Anchor Representations), a novel Transformer-based model designed for efficient sequence-to-sequence transduction over streams. STAR dynamically segments input streams to create compressed anchor representations, achieving nearly lossless (12x) compression in Automatic Speech Recognition (ASR) and outperforming existing methods. Moreover, STAR demonstrates superior segmentation and latency-quality trade-offs in simultaneous Speech Translation, optimizing latency, memory footprint, and quality."
}