Zero-Shot Text Matching for Automated Auditing using Sentence Transformers

TL;DR

This paper explores the use of Sentence-Bert, a transformer-based model, for zero-shot text matching in automated auditing, demonstrating its robustness across in- and out-of-domain financial documents without requiring extensive annotated data.

Contribution

It introduces the application of Sentence-Bert for zero-shot semantic similarity in financial passages, highlighting its effectiveness in industrial auditing contexts.

Findings

Model performs well on unseen financial documents

Robustness demonstrated across different domains

Reduces need for annotated training data

Abstract

Natural language processing methods have several applications in automated auditing, including document or passage classification, information retrieval, and question answering. However, training such models requires a large amount of annotated data which is scarce in industrial settings. At the same time, techniques like zero-shot and unsupervised learning allow for application of models pre-trained using general domain data to unseen domains. In this work, we study the efficiency of unsupervised text matching using Sentence-Bert, a transformer-based model, by applying it to the semantic similarity of financial passages. Experimental results show that this model is robust to documents from in- and out-of-domain data.