Zero-Shot Text Matching for Automated Auditing using Sentence Transformers
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.
Cite
@article{arxiv.2211.07716,
title = {Zero-Shot Text Matching for Automated Auditing using Sentence Transformers},
author = {David Biesner and Maren Pielka and Rajkumar Ramamurthy and Tim Dilmaghani and Bernd Kliem and Rüdiger Loitz and Rafet Sifa},
journal= {arXiv preprint arXiv:2211.07716},
year = {2022}
}
Comments
To be published in proceedings of IEEE International Conference on Machine Learning Applications IEEE ICMLA 2022