D
2023
bot.zen at LangLearn: regressing towards interpretability
STEMLE, Egon, Martina TEBALDINI, Francesca BONANNI, Filippo PELLEGRINO, Paolo BRASOLIN et. al.
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
Other information
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Germany
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
electronic version available online
RIV identification code
RIV/00216224:14330/23:00133120
Organization unit
Faculty of Informatics
Keywords in English
system description; langlearn; evalita; shared task; regression; MALT-IT2; bot.zen
Tags
International impact, Reviewed
V originále
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.
Displayed: 4/11/2024 18:57