STEMLE, Egon, Martina TEBALDINI, Francesca BONANNI, Filippo PELLEGRINO, Paolo BRASOLIN, Greta H. FRANZINI, Jennifer-Carmen FREY, Olga LOPOPOLO and Stefania SPINA. bot.zen at LangLearn: regressing towards interpretability. Online. In M. Lai, S. Menini, M. Polignano,V. Russo, R. Sprugnoli, G. Venturi. Proceedings of the Eighth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Parma: CEUR.org, 2023, p. 1-5. ISSN 1613-0073.
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Basic information
Original name bot.zen at LangLearn: regressing towards interpretability
Authors STEMLE, Egon (276 Germany, guarantor, belonging to the institution), Martina TEBALDINI (380 Italy), Francesca BONANNI (380 Italy), Filippo PELLEGRINO (380 Italy), Paolo BRASOLIN (380 Italy), Greta H. FRANZINI (826 United Kingdom of Great Britain and Northern Ireland), Jennifer-Carmen FREY (40 Austria), Olga LOPOPOLO (380 Italy) and Stefania SPINA (380 Italy).
Edition Parma, Proceedings of the Eighth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, p. 1-5, 5 pp. 2023.
Publisher CEUR.org
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Germany
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
WWW URL
RIV identification code RIV/00216224:14330/23:00133120
Organization unit Faculty of Informatics
ISSN 1613-0073
Keywords in English system description; langlearn; evalita; shared task; regression; MALT-IT2; bot.zen
Tags firank_B
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 8/4/2024 21:49.
Abstract
This article describes the bot.zen system that participated in the Language Learning Development (LangLearn) shared task of the EVALITA 2023 campaign. We developed a simple machine learning system with good interpretability for later use, and used the shared task as an opportunity to provide Master’s students with hands-on training and practical experience in NLP.
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