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

Publisher

CEUR.org

Other information

Language

English

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

References:

RIV identification code

RIV/00216224:14330/23:00133120

Organization unit

Faculty of Informatics

ISSN

Keywords in English

system description; langlearn; evalita; shared task; regression; MALT-IT2; bot.zen

Tags

Tags

International impact, Reviewed
Změněno: 8/4/2024 21:49, RNDr. Pavel Šmerk, Ph.D.

Abstract

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.