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

Zero-Shot Text Matching for Automated Auditing using Sentence Transformers