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arXiv · 2211.07716

Zero-Shot Text Matching for Automated Auditing using Sentence Transformers

David Biesner·Maren Pielka·Rajkumar Ramamurthy·Tim Dilmaghani·Bernd Kliem·Rüdiger Loitz·Rafet Sifa
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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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David Biesner, Maren Pielka, Rajkumar Ramamurthy, Tim Dilmaghani, Bernd Kliem, Rüdiger Loitz, Rafet Sifa. 2022-10-28. Zero-Shot Text Matching for Automated Auditing using Sentence Transformers. https://arxiv.org/abs/2211.07716

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