Daniela Occhipinti, A. Tesei, Maria Iacono, C. Aliprandi, Lorenzo De Mattei
{"title":"ItaliaNLP @ TAG-IT: UmBERTo for Author Profiling at TAG-it 2020 (short paper)","authors":"Daniela Occhipinti, A. Tesei, Maria Iacono, C. Aliprandi, Lorenzo De Mattei","doi":"10.4000/BOOKS.AACCADEMIA.7297","DOIUrl":null,"url":null,"abstract":"In this paper we describe the systems we used to participate in the task TAG-it of EVALITA 2020. The first system we developed uses linear Support Vector Machine as learning algorithm. The other two systems are based on the pretrained Italian Language Model UmBERTo: one of them has been developed following the Multi-Task Learning approach, while the other following the Single-Task Learning approach. These systems have been evaluated on TAG-it official test sets and ranked first in all the TAG-it subtasks, demonstrating the validity of the approaches we followed.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
In this paper we describe the systems we used to participate in the task TAG-it of EVALITA 2020. The first system we developed uses linear Support Vector Machine as learning algorithm. The other two systems are based on the pretrained Italian Language Model UmBERTo: one of them has been developed following the Multi-Task Learning approach, while the other following the Single-Task Learning approach. These systems have been evaluated on TAG-it official test sets and ranked first in all the TAG-it subtasks, demonstrating the validity of the approaches we followed.