Paper: arXiv 2311.08533

Abstract

This article provides an understanding of Natural Language Processing techniques in the framework of financial regulation, more specifically in order to perform semantic matching search between rules and policy when no dataset is available for supervised learning. We outline how to outperform simple pre-trained sentences-transformer models using freely available resources and explain the mathematical concepts behind the key building blocks of Natural Language Processing.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 3.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper explains advanced NLP concepts and mathematical formulations like co-occurrence matrices, SVD, and Skip-gram training objectives, but provides no implementation details, backtesting results, or code, focusing instead on theoretical understanding for financial regulation.

Research Flowchart

  flowchart TD
  Start["Research Goal: Semantic Matching<br>for Financial Regulation<br>(No Labeled Dataset)"]
  
  Data -->|Free Text Resources| Embedding
  subgraph Data["Data / Inputs"]
      Rules["Regulatory Rules"]
      Policies["Policy Documents"]
  end

  subgraph Methodology["Methodology"]
      Embedding["Fine-tune Sentence Transformers<br>(Unsupervised Learning)"]
      Match["Semantic Matching<br>Vector Similarity Search"]
      Eval["Evaluate against<br>Baseline Models"]
  end

  subgraph Outcomes["Key Findings / Outcomes"]
      Result["Outperforms<br>Pre-trained Baselines"]
      Insight["Mathematical Understanding<br>of NLP Building Blocks"]
  end

  Start --> Data
  Embedding --> Match
  Match --> Eval
  Eval --> Result
  Eval --> Insight