Report
Zero-Shot Text Matching for Automated Auditing using Sentence Transformers
| العنوان: | Zero-Shot Text Matching for Automated Auditing using Sentence Transformers |
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| المؤلفون: | Biesner, David, Pielka, Maren, Ramamurthy, Rajkumar, Dilmaghani, Tim, Kliem, Bernd, Loitz, Rüdiger, Sifa, Rafet |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Machine Learning |
| الوصف: | 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. Comment: To be published in proceedings of IEEE International Conference on Machine Learning Applications IEEE ICMLA 2022 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2211.07716 |
| رقم الانضمام: | edsarx.2211.07716 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |