Grunnleggende konsepter
Leveraging knowledge from semantically and contextually similar instances can enhance the performance of rhetorical role classifiers in legal documents, particularly in addressing challenges such as label imbalance and intricate role intertwining.
Sammendrag
The content discusses the task of Rhetorical Role Labeling (RRL) in legal documents, which involves assigning functional roles to sentences in a document, such as preamble, factual content, evidence, reasoning, etc. The task faces several challenges, including contextual dependencies, intertwined rhetorical roles, limited annotated data, and label imbalance.
The authors propose two approaches to leverage knowledge from semantically and contextually similar instances to enhance RRL performance:
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Inference-based Approach:
- Interpolation with k-Nearest Neighbors (kNN): Interpolate the label distribution predicted by the baseline model with the distribution derived from the k-nearest training instances.
- Interpolation with Single Prototype: Use a single prototype per label, representing the average of contextualized embeddings of sentences with the same label.
- Interpolation with Multiple Prototypes: Use multiple prototypes per label to capture diverse variations within the same label.
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Training-based Approach:
- Contrastive Learning: Bring instances with the same label closer in the embedding space and push away instances with different labels.
- Discourse-aware Contrastive Learning: Incorporate relative position information to encourage instances with the same label and in close proximity within the document to be closer in the embedding space.
- Single Prototypical Learning: Use a single prototype per label as a guiding point during training.
- Multi-Prototypical Learning: Use multiple prototypes per label to capture diverse variations within the same label.
The authors evaluate their proposed methods on four datasets from the Indian legal domain and observe significant improvements, particularly in the challenging macro-F1 metric. They also assess the cross-domain generalizability of their methods, demonstrating their effectiveness in transferring knowledge across diverse legal domains.
Statistikk
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Sitater
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