@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Assisting Corpus Annotation: Automatic BIO-Tagging of Clause-Like Units in Polish Sign Language. A Pilot Study on Corpus Data
%A Mostowski, Piotr
%A Kuder, Anna
%A Wójcicka, Joanna
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Mesch, Johanna
%Y Schulder, Marc
%S Proceedings of the LREC 2026 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F mostowski-etal-2026-assisting
%X 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.
%R 10.63317/364zmis7ppgo
%U https://aclanthology.org/2026.signlang-1.37/
%U https://doi.org/10.63317/364zmis7ppgo
%P 351-360
Markdown (Informal)
[Assisting Corpus Annotation: Automatic BIO-Tagging of Clause-Like Units in Polish Sign Language. A Pilot Study on Corpus Data](https://aclanthology.org/2026.signlang-1.37/) (Mostowski et al., SignLang 2026)
ACL