369 citations · 1.2k across the 41 of their papers we have counts for
5 papers · 1 filter
Sentence Embeddings and High-speed Similarity Search for Fast Computer Assisted Annotation of Legal Documents
Hannes Westermann, Jaromir Savelka, Vern R. Walker +2
Human-performed annotation of sentences in legal documents is an important prerequisite to many machine learning based systems supporting legal tasks. Typically, the annotation is…
Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains
Jaromir Savelka, Hannes Westermann, Karim Benyekhlef +15
In this paper, we examine the use of multi-lingual sentence embeddings to transfer predictive models for functional segmentation of adjudicatory decisions across jurisdictions, leg…
Cross-Domain Generalization and Knowledge Transfer in Transformers Trained on Legal Data
Jaromir Savelka, Hannes Westermann, Karim Benyekhlef
We analyze the ability of pre-trained language models to transfer knowledge among datasets annotated with different type systems and to generalize beyond the domain and dataset the…
Discovering Explanatory Sentences in Legal Case Decisions Using Pre-trained Language Models
Jaromir Savelka, Kevin D. Ashley
Legal texts routinely use concepts that are difficult to understand. Lawyers elaborate on the meaning of such concepts by, among other things, carefully investigating how have they…
Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain
Hannes Westermann, Jaromir Savelka, Vern R. Walker +2
In this paper, we present a method of building strong, explainable classifiers in the form of Boolean search rules. We developed an interactive environment called CASE (Computer As…