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ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks

T. Y. S. S Santosh, Tuan-Quang Vuong, Matthias Grabmair

TL;DR

ChronosLex addresses temporal drift in legal multi-label text classification by introducing incremental fine-tuning over chronological data and evaluating robustness with both fixed and streaming time splits. The approach integrates continual learning and temporal-invariant strategies to balance past knowledge with new information, finding that continual learning generally enhances temporal generalization while temporal invariants often underperform. Across six datasets, streaming evaluation reveals consistent gains for continual methods, underscoring the importance of time-aware evaluation. The work advocates for broader adoption of streaming protocols and sets the stage for applying incremental training to other temporally sensitive legal tasks.

Abstract

This study investigates the challenges posed by the dynamic nature of legal multi-label text classification tasks, where legal concepts evolve over time. Existing models often overlook the temporal dimension in their training process, leading to suboptimal performance of those models over time, as they treat training data as a single homogeneous block. To address this, we introduce ChronosLex, an incremental training paradigm that trains models on chronological splits, preserving the temporal order of the data. However, this incremental approach raises concerns about overfitting to recent data, prompting an assessment of mitigation strategies using continual learning and temporal invariant methods. Our experimental results over six legal multi-label text classification datasets reveal that continual learning methods prove effective in preventing overfitting thereby enhancing temporal generalizability, while temporal invariant methods struggle to capture these dynamics of temporal shifts.

ChronosLex: Time-aware Incremental Training for Temporal Generalization of Legal Classification Tasks

TL;DR

ChronosLex addresses temporal drift in legal multi-label text classification by introducing incremental fine-tuning over chronological data and evaluating robustness with both fixed and streaming time splits. The approach integrates continual learning and temporal-invariant strategies to balance past knowledge with new information, finding that continual learning generally enhances temporal generalization while temporal invariants often underperform. Across six datasets, streaming evaluation reveals consistent gains for continual methods, underscoring the importance of time-aware evaluation. The work advocates for broader adoption of streaming protocols and sets the stage for applying incremental training to other temporally sensitive legal tasks.

Abstract

This study investigates the challenges posed by the dynamic nature of legal multi-label text classification tasks, where legal concepts evolve over time. Existing models often overlook the temporal dimension in their training process, leading to suboptimal performance of those models over time, as they treat training data as a single homogeneous block. To address this, we introduce ChronosLex, an incremental training paradigm that trains models on chronological splits, preserving the temporal order of the data. However, this incremental approach raises concerns about overfitting to recent data, prompting an assessment of mitigation strategies using continual learning and temporal invariant methods. Our experimental results over six legal multi-label text classification datasets reveal that continual learning methods prove effective in preventing overfitting thereby enhancing temporal generalizability, while temporal invariant methods struggle to capture these dynamics of temporal shifts.
Paper Structure (18 sections, 3 figures, 3 tables)

This paper contains 18 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Evaluation Strategy
  4. Datasets
  5. ChronosLex
  6. Continual Learning Methods
  7. Temporal Invariant Methods
  8. Experiments
  9. Base Model & Metrics
  10. Results on Eval-Fix Setting
  11. Results on Eval-Stream Setting
  12. Conclusion
  13. Implementation Details
  14. Continual Learning methods specific Hyper-parameters
  15. Temporal Invariant learning methods specific Hyper-parameters
  16. ...and 3 more sections

Figures (3)

  • Figure 1: Different evaluation and training strategies employed in this work, to assess and improve temporal generalization of models for legal classification tasks.
  • Figure 2: Results on Eval-Fix Setting over six datasets.
  • Figure 3: Results on Eval-Stream Setting over six datasets.