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We analyze the ability of pre-trained language models to transfer knowledgeamong datasets annotated with different type systems and to generalize beyondthe domain and dataset they were trained on. We create a meta task, overmultiple datasets focused on the prediction of rhetorical roles. Prediction ofthe rhetorical role a sentence plays in a case decision is an important andoften studied task in AI & Law. Typically, it requires the annotation of alarge number of sentences to train a model, which can be time-consuming andexpensive. Further, the application of the models is restrained to the samedataset it was trained on. We fine-tune language models and evaluate theirperformance across datasets, to investigate the models' ability to generalizeacross domains. Our results suggest that the approach could be helpful inovercoming the cold-start problem in active or interactvie learning, and showsthe ability of the models to generalize across datasets and domains.

Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data