@inproceedings{kermani-etal-2026-temporal,
    title = "Temporal-Linguistic Adaptive Streaming for Continuous Sign Language Translation",
    author = "Kermani, Arshia  and
      Irani, Habib  and
      Ross, Deautaun  and
      Metsis, Vangelis",
    editor = "Yan, Qianqi  and
      Montariol, Syrielle  and
      Fan, Yue  and
      Gu, Jing  and
      Pan, Jiayi  and
      Li, Manling  and
      Kordjamshidi, Parisa  and
      Suhr, Alane  and
      Wang, Xin Eric",
    booktitle = "Proceedings of the 4th Workshop on Advances in Language and Vision Research ({ALVR})",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.alvr-main.21/",
    pages = "239--248",
    ISBN = "979-8-89176-398-2",
    abstract = "Real-time sign language translation must generate text incrementally as signs arrive, yet existing streaming policies treat glosses as a flat token sequence and discard the temporal rhythm of signing. Inter-gloss pauses reliably mark sentence boundaries in continuous discourse, but policies such as Wait-k cause arbitrary cross-boundary fragmentation. We propose Temporal-Linguistic Adaptive Streaming (TLAS), which fuses a Temporal Pause Detector (TPD, tracking inter-gloss interval statistics via an exponential moving average) and a Linguistic Readiness Estimator (LRE, a trained neural head on a frozen T5 encoder) through an Adaptive Fusion Gate (AFG). A proactive timeout fires before the next gloss arrives when the inter-gloss gap exceeds a threshold, producing clean sentence segmentation without oracle boundary information. We also contribute a synthetic discourse dataset of 1,400 ASL discourse groups with LLM-generated per-gloss timestamps and introduce a continuous-stream evaluation paradigm requiring autonomous boundary detection from an unbroken gloss stream. Under such conditions, TLAS significantly outperforms current heuristic baselines, such as Wait-k, and methods relying solely on linguistic content."
}