Text Model for the Automatic Scoring of Business Letter Writing

This article describes a text model designed to automatically score a cohesive text in the form of a letter on a theme. The scoring parameters are formulated and formalized in the form of 14 criteria with the help of expert English language teachers. The criteria include parameters related to the an...

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Vydané v:	Automatic control and computer sciences Ročník 57; číslo 7; s. 828 - 840
Hlavní autori:	Zafievsky, D. D., Lagutina, N. S., Melnikova, O. A., Poletaev, A. Y.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Moscow Pleiades Publishing 01.12.2023 Springer Nature B.V
Predmet:	Algorithms Automation Communication Computer Science Control Structures and Microprogramming Criteria Deep learning Dictionaries English language Error analysis Essays Foreign language learning Graphical representations Language proficiency Machine learning Morphology Natural language processing Neural networks Parameters Semantics Sentences Skills Teachers Texts Writing automatic text processing automated essay scoring business letter writing text options
ISSN:	0146-4116, 1558-108X
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Abstract	This article describes a text model designed to automatically score a cohesive text in the form of a letter on a theme. The scoring parameters are formulated and formalized in the form of 14 criteria with the help of expert English language teachers. The criteria include parameters related to the analysis of vocabulary, including the features of the data domain, text subject, writing style and format, and logical connection in sentences. The authors have developed algorithms for determining the corresponding numerical characteristics using methods and tools for automatic text analysis. The algorithms are based on the analysis of the composition and structure of sentences, using data from specialized dictionaries. The characteristics are focused on checking business e-mails, but can be adapted to the analysis of other written texts, for example, by replacing dictionaries. Based on the developed algorithms, a system for automatic text scoring is created. An experiment is carried out to analyze the results of this system’s operation on a corpus of 20 texts, previously marked up by English teachers. Automatic scoring and the scoring of experts are compared using heat maps and the the UMAP two-dimensional representation of vectors applied to the characteristic text vectors. In most cases, there are no significant differences between the scores; moreover, automatic scoring turns out to be more objective. Thus, the developed model successfully copes with this task and can be used to evaluate texts written by humans. The results will be used for automatic student language profiling. The advantages of the model lie in the good interpretability of the results, credibility, and development prospects.
AbstractList	This article describes a text model designed to automatically score a cohesive text in the form of a letter on a theme. The scoring parameters are formulated and formalized in the form of 14 criteria with the help of expert English language teachers. The criteria include parameters related to the analysis of vocabulary, including the features of the data domain, text subject, writing style and format, and logical connection in sentences. The authors have developed algorithms for determining the corresponding numerical characteristics using methods and tools for automatic text analysis. The algorithms are based on the analysis of the composition and structure of sentences, using data from specialized dictionaries. The characteristics are focused on checking business e-mails, but can be adapted to the analysis of other written texts, for example, by replacing dictionaries. Based on the developed algorithms, a system for automatic text scoring is created. An experiment is carried out to analyze the results of this system’s operation on a corpus of 20 texts, previously marked up by English teachers. Automatic scoring and the scoring of experts are compared using heat maps and the the UMAP two-dimensional representation of vectors applied to the characteristic text vectors. In most cases, there are no significant differences between the scores; moreover, automatic scoring turns out to be more objective. Thus, the developed model successfully copes with this task and can be used to evaluate texts written by humans. The results will be used for automatic student language profiling. The advantages of the model lie in the good interpretability of the results, credibility, and development prospects. This article describes a text model designed to automatically score a cohesive text in the form of a letter on a theme. The scoring parameters are formulated and formalized in the form of 14 criteria with the help of expert English language teachers. The criteria include parameters related to the analysis of vocabulary, including the features of the data domain, text subject, writing style and format, and logical connection in sentences. The authors have developed algorithms for determining the corresponding numerical characteristics using methods and tools for automatic text analysis. The algorithms are based on the analysis of the composition and structure of sentences, using data from specialized dictionaries. The characteristics are focused on checking business e-mails, but can be adapted to the analysis of other written texts, for example, by replacing dictionaries. Based on the developed algorithms, a system for automatic text scoring is created. An experiment is carried out to analyze the results of this system’s operation on a corpus of 20 texts, previously marked up by English teachers. Automatic scoring and the scoring of experts are compared using heat maps and the the UMAP two-dimensional representation of vectors applied to the characteristic text vectors. In most cases, there are no significant differences between the scores; moreover, automatic scoring turns out to be more objective. Thus, the developed model successfully copes with this task and can be used to evaluate texts written by humans. The results will be used for automatic student language profiling. The advantages of the model lie in the good interpretability of the results, credibility, and development prospects.
Author	Poletaev, A. Y. Lagutina, N. S. Zafievsky, D. D. Melnikova, O. A.
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Copyright	Allerton Press, Inc. 2023. ISSN 0146-4116, Automatic Control and Computer Sciences, 2023, Vol. 57, No. 7, pp. 828–840. © Allerton Press, Inc., 2023. Russian Text © The Author(s), 2022, published in Modelirovanie i Analiz Informatsionnykh Sistem, 2022, Vol. 29, No. 4, pp. 348–365. Allerton Press, Inc. 2023.
Copyright_xml	– notice: Allerton Press, Inc. 2023. ISSN 0146-4116, Automatic Control and Computer Sciences, 2023, Vol. 57, No. 7, pp. 828–840. © Allerton Press, Inc., 2023. Russian Text © The Author(s), 2022, published in Modelirovanie i Analiz Informatsionnykh Sistem, 2022, Vol. 29, No. 4, pp. 348–365. – notice: Allerton Press, Inc. 2023.
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References_xml	– reference: VajjalaS.Automated assessment of non-native learner essays: Investigating the role of linguistic featuresInt. J. Artif. Intell. Educ.2018287910510.1007/s40593-017-0142-3 – reference: Farag, Yo., Yannakoudakis, H., and Briscoe, T., Neural automated essay scoring and coherence modeling for adversarially crafted input, Proc. 2018 Conf. of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Walker, M., Ji, H., and Stent, A., Eds., New Orleans: Association for Computational Linguistics, 2018, vol. 1, pp. 263–271. https://doi.org/10.18653/v1/n18-1024 – reference: YangR.CaoJ.WenZ.WuYo.HeX.Enhancing automated essay scoring performance via fine-tuning pre-trained language models with combination of regression and ranking, Findings of the Association for Computational Linguistics: EMNLP 2020202010.18653/v1/2020.findings-emnlp.141 – reference: SobolevaN.P.NilovaM.A.Teaching writing to students of humanitarian specialties using modern educational technologiesKazanskii Vestn. Molodykh Uchenykh201825759 – reference: Taghipour, K. and Ng, H., A neural approach to automated essay scoring, Proc. 2016 Conf. on Empirical Methods in Natural Language Processing, Su, J., Duh, K., and Carreras, X., Eds., Austin, Texas: Association for Computational Linguistics, 2016, pp. 1882–1891. https://doi.org/10.18653/v1/d16-1193 – reference: ZhuW.SunYu.Automated essay scoring system using multi-model machine learning, Computer Science & Information Technology (CS & IT)202010.5121/csit.2020.101211 – reference: Al-Bargi, A., Exploring online writing assessment amid Covid-19: Challenges and opportunities from teachers’ perspectives, Arab World Engl. J., 2022, no. 2, pp. 3–21. https://doi.org/10.24093/awej/covid2.1 – reference: YangYu.ZhongJ.Automated essay scoring via example-based learning, Web Engineering. ICWE 20212021ChamSpringer10.1007/978-3-030-74296-6_16 – reference: Al-MwzaijiKh.N.A.AlzubiA.A.F.Online self-evaluation: The EFL writing skills in focus, Asian-Pac.J. Second Foreign Lang. Educ.2022711610.1186/s40862-022-00135-8 – reference: John BernardinH.ThomasonS.Ronald BuckleyM.KaneJ.Rater rating-level bias and accuracy in performance appraisals: The impact of rater personality, performance management competence, and rater accountabilityHum. Resour. Manage.20165532134010.1002/hrm.21678 – reference: FareedM.AshrafA.BilalM.ESL learners' writing skills: Problems, factors and suggestionsJ. Educ. Soc. Sci.20164839410.20547/jess0421604201 – reference: AomiI.TsutsumiE.UtoM.UenoM.Integration of automated essay scoring models using item response theory, Artificial Intelligence in Education. AIED 2021, Roll, I., McNamara, D.2021ChamSpringer10.1007/978-3-030-78270-2_9 – reference: Xia, L., Liu, J., and Zhang, Z., Automatic essay scoring model based on two-layer bi-directional long-short term memory network, Proc. 2019 3rd Int. Conf. on Computer Science and Artificial Intelligence, Normal, Ill., 2019, New York: Association for Computing Machinery, 2019, pp. 133–137. https://doi.org/10.1145/3374587.3374596 – reference: Darwish, S.M. and Mohamed, S.K., Automated essay evaluation based on fusion of fuzzy ontology and latent semantic analysis, The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019). AMLTA 2019, Hassanien, A., Azar, A., Gaber, T., Bhatnagar, R., and Tolba, M.F., Eds., Advances in Intelligent Systems and Computing, Cham: Springer, 2019, pp. 566–575. https://doi.org/10.1007/978-3-030-14118-9_57 – reference: HusseinM.A.HassanH.NassefM.Automated language essay scoring systems: A literature reviewPeerJ Comput. Sci.20195e20810.7717/peerj-cs.208338168617924549 – reference: Ke, Z. and Ng, V., Automated essay scoring: A survey of the state of the art, Proc. Twenty-Eighth Int. 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Snippet	This article describes a text model designed to automatically score a cohesive text in the form of a letter on a theme. The scoring parameters are formulated...
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SubjectTerms	Algorithms Automation Communication Computer Science Control Structures and Microprogramming Criteria Deep learning Dictionaries English language Error analysis Essays Foreign language learning Graphical representations Language proficiency Machine learning Morphology Natural language processing Neural networks Parameters Semantics Sentences Skills Teachers Texts Writing
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Title	Text Model for the Automatic Scoring of Business Letter Writing
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