Real-World Implementation and Integration of an Automatic Scoring System for Workplace Safety Courses in Italian

Author:

Arici Nicola12,Gerevini Alfonso1,Olivato Matteo1ORCID,Putelli Luca1,Sigalini Luca2,Serina Ivan1ORCID

Affiliation:

1. Department of Information Engineering, University of Brescia, Via Branze 38, 25121 Brescia, Italy

2. Mega Italia Media, Via Roncadelle 70A, 25030 Castel Mella, Italy

Abstract

Artificial Intelligence and Natural Language Processing techniques can have a very significant impact on the e-learning sector, with the introduction of chatbots, automatic correctors, or scoring systems. However, integrating such technologies into the business environment in an effective way is not a trivial operation, and it not only requires realising a model with good predictive performance, but also it requires the following: (i) a proper study of the task, (ii) a data collection process, (iii) a real-world evaluation of its utility. Moreover, it is also very important to build an entire IT infrastructure that connects the AI system with the company database, with the human employees, the users, etc. In this work, we present a real-world system, based on the state-of-the-art BERT model, which implements an automatic scoring system for open-ended questions written in Italian. More specifically, these questions pertain to the workplace safety courses which every worker must attend by law, often via e-learning platforms such as the one offered by Mega Italia Media. This article describes how our system has been designed, evaluated, and finally deployed for commercial use with complete integration with the other services provided by the company.

Publisher

MDPI AG

Subject

Computer Networks and Communications

Reference51 articles.

1. A BERT-Based Scoring System for Workplace Safety Courses in Italian;Dovier;Lecture Notes in Computer Science, Proceedings of the AIxIA 2022—Advances in Artificial Intelligence—XXIst International Conference of the Italian Association for Artificial Intelligence, AIxIA 2022, Udine, Italy, 28 November–2 December 2022,2022

2. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding;Burstein;Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019,2019

3. Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., and Garnett, R. (2017, January 4–9). Attention is All you Need. Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA.

4. Carpuat, M., de Marneffe, M., and Ruíz, I.V.M. (2022, January 10–15). On the Use of Bert for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022, Seattle, WA, USA.

5. Inui, K., Jiang, J., Ng, V., and Wan, X. (2019, January 3–7). SciBERT: A Pretrained Language Model for Scientific Text. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3