Zero-Shot Text Matching for Automated Auditing using Sentence Transformers
David Biesner, Maren Pielka, Rajkumar Ramamurthy, Tim Dilmaghani,, Bernd Kliem, R\"udiger Loitz, Rafet Sifa

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.
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Taxonomy
TopicsTopic Modeling · Text and Document Classification Technologies · Advanced Text Analysis Techniques
