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Zero-Shot Text Matching for Automated Auditing using Sentence Transformers

2022-10-28 11:52:16
David Biesner, Maren Pielka, Rajkumar Ramamurthy, Tim Dilmaghani, Bernd Kliem, Rüdiger Loitz, Rafet Sifa
arXiv_CL
             
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.

Abstract (translated)

URL

https://arxiv.org/abs/2211.07716

PDF

https://arxiv.org/pdf/2211.07716.pdf

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