@inproceedings{mostowski-etal-2026-assisting,
    title = "Assisting Corpus Annotation: Automatic {BIO}-Tagging of Clause-Like Units in {P}olish {S}ign {L}anguage. A Pilot Study on Corpus Data",
    author = "Mostowski, Piotr  and
      Kuder, Anna  and
      W{\'o}jcicka, Joanna",
    editor = "Efthimiou, Eleni  and
      Fotinea, Stavroula-Evita  and
      Hanke, Thomas  and
      Hochgesang, Julie A.  and
      Mesch, Johanna  and
      Schulder, Marc",
    booktitle = "Proceedings of the {LREC} 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.signlang-1.37/",
    doi = "10.63317/364zmis7ppgo",
    pages = "351--360",
    abstract = "The creation of large-scale sign language corpora is often bottlenecked by the labour-intensive process of multi-layered annotation that requires manual analysis. One of the annotation steps is the challenging and time-consuming task of segmenting continuous signing into clause-like-units (CLUs). In this paper, we propose an automated segmentation framework for Polish Sign Language (PJM) designed to support manual annotation. To detect sentence boundaries, we adapt the Multi-Stage Temporal Convolutional Network (MS-TCN) architecture, enhanced with a Channel Attention mechanism, to effectively fuse multimodal skeleton features (hands, body, and face) extracted via MediaPipe. We evaluate the model on a diverse subset of the PJM Corpus (40 video files, 25 signers), containing nearly 16,000 manually annotated clauses prior to the start of this study. The proposed method achieves a Segmental F1-score of 75.43{\%} at IoU = 0.10 and 57.52{\%} at IoU = 0.50, demonstrating a strong capability in localising sentence boundaries. Furthermore, ablation studies reveal that fusing manual kinematics with non-manual prosodic cues (face) yields a significant performance gain (+13.6 pp) over unimodal baselines, empirically confirming the linguistic necessity of incorporating both manual and non-manual articulators in the process of sentence delimitation. The solution offers a viable means for reducing CLU annotation time by automatically generating high-quality clause boundary proposals."
}