mlsanityData to 14 Oct 2026

Multi-Source (Pre-)Training for Cross-Domain Measurement, Unit and Context Extraction

arXiv 2308.02951 ↗First posted 5 Aug 2023cs.CLcited by 1

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Abstract (arXiv)

We present a cross-domain approach for automated measurement and context extraction based on pre-trained language models. We construct a multi-source, multi-domain corpus and train an end-to-end extraction pipeline. We then apply multi-source task-adaptive pre-training and fine-tuning to benchmark the cross-domain generalization capability of our model. Further, we conceptualize and apply a task-specific error analysis and derive insights for future work. Our results suggest that multi-source training leads to the best overall results, while single-source training yields the best results for the respective individual domain. While our setup is successful at extracting quantity values and units, more research is needed to improve the extraction of contextual entities. We make the cross-domain corpus used in this work available online.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 13 of its 34 references that are in the map. First posted on arXiv 5 Aug 2023.

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13 of 34 references.

References · 13

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1 more
  1. 2021Counts@IITK at SemEval-2021 Task 8: SciBERT Based Entity And Semantic Relation Extraction For Scientific Datacited by 3

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  1. 2024Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstructioncited by 1