Summary:
Rhetorical Role Labeling (RRL) in legal sentences represents a key task for automating legal processes, such as semantic search, argument mining, or case summarization. This field faces significant challenges, including contextual dependency between sentences, complexity in role interrelation, scarcity of annotated data, and class imbalance. To address these issues, new methodologies have been proposed that leverage knowledge from semantically similar instances (so-called neighbors), improving model performance without requiring data expansion or retraining the base model from scratch.
Inference methods apply interpolations between current predictions and inferences derived from previously seen neighboring sentences, using techniques based on nearest neighbors or multiple prototypes. On the other hand, training methods employ contrastive and prototypical learning strategies integrated with the embedding space, also incorporating a novel contrastive loss function sensitive to legal discourse, which better captures contextual dependencies between phrases.
Through multiple experiments applied to four legal datasets from the Indian jurisdiction, it has been shown that these techniques not only improve performance on imbalanced data but also endow models with cross-domain generalization capability across different legal domains, a crucial factor for scalability. Cross-domain adaptability has been identified as a key limitation in traditional implementations, which often remain tied to specific court or country terminologies.
In this line of innovation, companies like Q2BSTUDIO, specialized in development and technology services, play an essential role by applying this type of research in practical solutions. Q2BSTUDIO works on the design and implementation of intelligent tools for the legal sector that integrate natural language processing (NLP) and machine learning, facilitating digital transformation and operational efficiency in law firms, public bodies, and legal companies. Thanks to Q2BSTUDIO's expertise, these advanced techniques can become scalable, ethically sustainable, and viable systems for international commercial adoption.
Authors:
Santosh T.Y.S.S, Hassan Sarwat, Ahmed Abdou, Matthias Grabmair – Technical University of Munich
