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    <abstract>While large general-purpose Transformer-based encoders excel at general language understanding, their performance diminishes in specialized domains like manufacturing due to a lack of exposure to domain-specific terminology and semantics. In this paper, we address this gap by introducing ManufactuBERT, a RoBERTa model continually pretrained on a large-scale corpus curated for the manufacturing domain. We present a comprehensive data processing pipeline to create this corpus from web data, involving an initial domain-specific filtering step followed by a multi-stage deduplication process that removes redundancies. Our experiments show that ManufactuBERT establishes a new state-of-the-art on a range of manufacturing-related NLP tasks, outperforming strong specialized baselines. More importantly, we demonstrate that training on our carefully deduplicated corpus significantly accelerates convergence, leading to a 33% reduction in training time and computational cost compared to training on the non-deduplicated dataset. The proposed pipeline offers a reproducible example for developing high-performing encoders in other specialized domains. Our model, code and curated corpus will be publicly available.</abstract>
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%0 Conference Proceedings
%T ManufactuBERT: Efficient Continual Pretraining for Manufacturing
%A Armingaud, Robin
%A Besancon, Romaric
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F armingaud-besancon-2026-manufactubert
%X While large general-purpose Transformer-based encoders excel at general language understanding, their performance diminishes in specialized domains like manufacturing due to a lack of exposure to domain-specific terminology and semantics. In this paper, we address this gap by introducing ManufactuBERT, a RoBERTa model continually pretrained on a large-scale corpus curated for the manufacturing domain. We present a comprehensive data processing pipeline to create this corpus from web data, involving an initial domain-specific filtering step followed by a multi-stage deduplication process that removes redundancies. Our experiments show that ManufactuBERT establishes a new state-of-the-art on a range of manufacturing-related NLP tasks, outperforming strong specialized baselines. More importantly, we demonstrate that training on our carefully deduplicated corpus significantly accelerates convergence, leading to a 33% reduction in training time and computational cost compared to training on the non-deduplicated dataset. The proposed pipeline offers a reproducible example for developing high-performing encoders in other specialized domains. Our model, code and curated corpus will be publicly available.
%R 10.63317/3c7yx4ewnj5m
%U https://aclanthology.org/2026.lrec-1.827/
%U https://doi.org/10.63317/3c7yx4ewnj5m
%P 10545-10555

Markdown (Informal)

[ManufactuBERT: Efficient Continual Pretraining for Manufacturing](https://aclanthology.org/2026.lrec-1.827/) (Armingaud & Besancon, LREC 2026)

• ManufactuBERT: Efficient Continual Pretraining for Manufacturing (Armingaud & Besancon, LREC 2026)

ACL

• Robin Armingaud and Romaric Besancon. 2026. ManufactuBERT: Efficient Continual Pretraining for Manufacturing. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 10545–10555, Palma de Mallorca, Spain. ELRA Language Resource Association.