Improving How We Label Legal Documents Using AI | HackerNoon

Table of Links

Abstract and 1. Introduction

2. Related Work

3. Task, Datasets, Baseline

4. RQ 1: Leveraging the Neighbourhood at Inference

   4.1. Methods

   4.2. Experiments

5. RQ 2: Leveraging the Neighbourhood at Training

   5.1. Methods

   5.2. Experiments

6. RQ 3: Cross-Domain Generalizability

7. Conclusion

8. Limitations

9. Ethics Statement

10. Bibliographical References

8. Limitations

One constraint in the current task formulation is that it restricts assigning a single label to each sentence, which may not fully account for the complexity of lengthy sentences that can encompass multiple rhetorical roles. To address this limitation, an alternative approach could involve reformulating the task as multi-label classification, enabling each sentence to be associated with more than one goldstandard rhetorical role. Another avenue for exploration is to shift from sentence-level segmentation towards a finer-grained approach at the phrase or sub-sentence level, necessitating the assignment of rhetorical roles to each phrase or sub-sentence while specifying the dependency relations between these segments (Tokala et al., 2023).

It’s important to acknowledge that while our cross-domain experiments have provided valuable insights into model generalizability, these evaluations have primarily focused on datasets originating from Indian courts, covering various domains within this single jurisdiction. The observed improved performance across these datasets could potentially be attributed to shared country-specific vocabulary and legal conventions. To ensure the robustness and generalizability in a broader context, it is imperative to expand the assessment to encompass diverse legal contexts across different countries and regions, where legal documents from may exhibit significant linguistic and structural variations.

9. Ethics Statement

The scope of this work is to provide new methods along with corresponding experiments to drive research forward in rhetorical role labeling, which is a pivotal task constituting the inaugural step in the legal document processing pipeline. Our experiments have been carried out on four publicly available datasets from different Indian courts. Though these decisions are not anonymized and contain the real names of the involved parties, we do not foresee any harm incurred by our experiments. We believe that our research contributes positively to the broader goals of advancing legal NLP and the development of AI-driven tools for legal professionals. By enhancing the automation of rhetorical role labeling, we can streamline legal document analysis and significantly benefit the legal field.

10. Bibliographical References

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