Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains
This addresses the challenge of limited adoption of AI in law due to legal system differences, offering a transferable solution.
The paper tackles the problem of transferring predictive models for functional segmentation of adjudicatory decisions across different languages, jurisdictions, and legal domains, finding that models generalize beyond their training contexts and that multi-context training improves robustness and performance.
In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, legal systems (common and civil law), languages, and domains (i.e. contexts). Mechanisms for utilizing linguistic resources outside of their original context have significant potential benefits in AI & Law because differences between legal systems, languages, or traditions often block wider adoption of research outcomes. We analyze the use of Language-Agnostic Sentence Representations in sequence labeling models using Gated Recurrent Units (GRUs) that are transferable across languages. To investigate transfer between different contexts we developed an annotation scheme for functional segmentation of adjudicatory decisions. We found that models generalize beyond the contexts on which they were trained (e.g., a model trained on administrative decisions from the US can be applied to criminal law decisions from Italy). Further, we found that training the models on multiple contexts increases robustness and improves overall performance when evaluating on previously unseen contexts. Finally, we found that pooling the training data from all the contexts enhances the models' in-context performance.