Multi-Source (Pre-)Training for Cross-Domain Measurement, Unit and Context Extraction
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.
Code (1)
Tasks
Domain GeneralizationSimilar Papers 제목 키워드 기반
SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency
Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generaliz…
Domain AdaptationDomain GeneralizationSource-Free Domain AdaptationCFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models
Channel foundation models (CFMs) are commonly evaluated in model-specific pipelines that differ in data, radio configurations, partitions, adaptation procedures, task definitions, and metrics, preventing reproducible com…
Beam PredictionImproving Generalizability of Hip Fracture Risk Prediction via Domain Adaptation Across Multiple Cohorts
Clinical risk prediction models often fail to be generalized across cohorts because underlying data distributions differ by clinical site, region, demographics, and measurement protocols. This limitation is particularly …
Domain AdaptationA measurement decoupling based fast algorithm for super-resolving point sources with multi-cluster structure
We consider the problem of resolving closely spaced point sources in one dimension from their Fourier data in a bounded domain. Classical subspace methods (e.g., MUSIC algorithm, Matrix Pencil method, etc.) show great su…
subspace methodsContradictory Structure Learning for Semi-supervised Domain Adaptation
Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the sou…
ClusteringDomain AdaptationSemi-supervised Domain Adaptation