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

Cited References (24)

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  2. SciBERT: A Pretrained Language Model for Scientific Text (2019)
  3. Language models are few-shot learners (2020)
  4. Indexing by latent semantic analysis (1990)
  5. Producing high-dimensional semantic spaces from lexical co-occurrence (1996)
  6. A maximum entropy approach to adaptive statistical language modelling (1996)
  7. A closer look at skip-gram modelling. (2006)
  8. Efficient estimation of word representations in vector space (2013)
and 16 more
  1. Distributed representations of words and phrases and their compositionality (2013)
  2. Formal algorithms for transformers (2022)
  3. Poly-encoders: Transformer architectures and pre-training strategies for fast and accurate multi-sentence scoring (2019)
  4. Bert: Pre-training of deep bidirectional transformers for language understanding (2018)
  5. Glove: Global vectors for word representation (2014)
  6. Sentence- Bert : Sentence embeddings using S iamese Bert -networks (2019)
  7. Attention is all you need (2017)
  8. BioBert : a pre-trained biomedical language representation model for biomedical text mining (2020)
  9. Tsdae: Using transformer-based sequential denoising auto-encoder for unsupervised sentence embedding learning (2021)
  10. Facenet: A unified embedding for face recognition and clustering (2015)
  11. Efficient natural language response suggestion for smart reply (2017)
  12. GPL : Generative pseudo labeling for unsupervised domain adaptation of dense retrieval (2022)
  13. Exploring the limits of transfer learning with a unified text-to-text transformer (2020)
  14. Molecule attention transformer (2002)
  15. An image is worth 16$\times$16 words: Transformers for image recognition at scale (2010)
  16. Studies in linguistic analysis (1957)

0 of 24 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.