Zero-Shot Text Matching for Automated Auditing using Sentence Transformers

• URL: http://arxiv.org/abs/2211.07716v1
• Date: Fri, 28 Oct 2022 11:52:16 GMT
• Title: Zero-Shot Text Matching for Automated Auditing using Sentence Transformers
• Authors: David Biesner, Maren Pielka, Rajkumar Ramamurthy, Tim Dilmaghani, Bernd Kliem, R\"udiger Loitz, Rafet Sifa
• Abstract summary: 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.
• Score: 0.3078691410268859
• License: http://creativecommons.org/licenses/by/4.0/
• 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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