Multi-Source (Pre-)Training for Cross-Domain Measurement, Unit and
  Context Extraction
Yueling Li Sebastian Martschat Simone Paolo Ponzetto
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.