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The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification

EasyChair Preprint no. 6935

6 pagesDate: October 26, 2021

Abstract

We aim to highlight an interesting trend to contribute to the ongoing debate around advances within legal Natural Language Processing. Recently, the focus for most legal text classification tasks has shifted towards large pre-trained deep learning models such as BERT. In this paper, we show that a more traditional approach based on Support Vector Machine classifiers reaches surprisingly competitive performance with BERT-based models on the classification tasks in the LexGLUE benchmark. We also highlight that error reduction obtained by using specialised BERT-based models over baselines is noticeably smaller in the legal domain when compared to general language tasks. We present and discuss three hypotheses as potential explanations for these results to support future discussions.

Keyphrases: machine learning, Natural Language Processing, text classification

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:6935,
  author = {Benjamin Clavié and Marc Alphonsus},
  title = {The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification},
  howpublished = {EasyChair Preprint no. 6935},

  year = {EasyChair, 2021}}
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