Pub Date : 1900-01-01DOI: 10.4000/BOOKS.AACCADEMIA.7207
S. Kayalvizhi, D. Thenmozhi, Aravindan Chandrabose
Stance detection refers to the detection of one’s opinion about the target from their statements. The aim of sardistance task is to classify the Italian tweets into classes of favor, against or no feeling towards the target. The task has two sub-tasks : in Task A, the classification has to be done by considering only the textual meaning whereas in Task B the tweets must be classified by considering the contextual information along with the textual meaning. We have presented our solution to detect the stance utilizing only the textual meaning (Task A) using encoder-decoder model and transformers. Among these two approaches, simple transformers have performed better than the encoder-decoder model with an average F1-score of 0.4707.
{"title":"SSN NLP @ SardiStance : Stance Detection from Italian Tweets using RNN and Transformers (short paper)","authors":"S. Kayalvizhi, D. Thenmozhi, Aravindan Chandrabose","doi":"10.4000/BOOKS.AACCADEMIA.7207","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7207","url":null,"abstract":"Stance detection refers to the detection of one’s opinion about the target from their statements. The aim of sardistance task is to classify the Italian tweets into classes of favor, against or no feeling towards the target. The task has two sub-tasks : in Task A, the classification has to be done by considering only the textual meaning whereas in Task B the tweets must be classified by considering the contextual information along with the textual meaning. We have presented our solution to detect the stance utilizing only the textual meaning (Task A) using encoder-decoder model and transformers. Among these two approaches, simple transformers have performed better than the encoder-decoder model with an average F1-score of 0.4707.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130083990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4000/BOOKS.AACCADEMIA.7092
Simone Giorgioni, Marcello Politi, Samir Salman, R. Basili, D. Croce
English. This paper describes the UNITOR system that participated to the Stance Detection in Italian tweets (Sardistance) task within the context of EVALITA 2020. UNITOR implements a transformer-based architecture whose accuracy is improved by adopting a Transfer Learning technique. In particular, this work investigates the possible contribution of three auxiliary tasks related to Stance Detection, i.e., Sentiment Detection, Hate Speech Detection and Irony Detection. Moreover, UNITOR relies on an additional dataset automatically downloaded and labeled through distant supervision. The UNITOR system ranked first in Task A within the competition. This confirms the effectiveness of Transformer-based architectures and the beneficial impact of the adopted strategies. Italiano. Questo lavoro descrive UNITOR, uno dei sistemi partecipanti allo Stance Detection in Italian tweet (SardiStance) task. UNITOR implementa un’architettura neurale basata su Transformer, la cui accuratezza viene migliorata applicando un metodo di Transfer Learning, che sfrutta le informazioni di tre task ausiliari, ovvero Sentiment Detection, Hate Speech Detection e Irony Detection. Inoltre, l’addestramento di UNITOR puó contare su un insieme di dati scaricati ed etichettati automaticamente applicando un semplice metodo di Distant Supervision. Il sistema si é classificato al primo posto nella competizione, confermando l’efficacia delle architetture basate su Transformer e il contributo delle strategie
{"title":"UNITOR @ Sardistance2020: Combining Transformer-based Architectures and Transfer Learning for Robust Stance Detection","authors":"Simone Giorgioni, Marcello Politi, Samir Salman, R. Basili, D. Croce","doi":"10.4000/BOOKS.AACCADEMIA.7092","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7092","url":null,"abstract":"English. This paper describes the UNITOR system that participated to the Stance Detection in Italian tweets (Sardistance) task within the context of EVALITA 2020. UNITOR implements a transformer-based architecture whose accuracy is improved by adopting a Transfer Learning technique. In particular, this work investigates the possible contribution of three auxiliary tasks related to Stance Detection, i.e., Sentiment Detection, Hate Speech Detection and Irony Detection. Moreover, UNITOR relies on an additional dataset automatically downloaded and labeled through distant supervision. The UNITOR system ranked first in Task A within the competition. This confirms the effectiveness of Transformer-based architectures and the beneficial impact of the adopted strategies. Italiano. Questo lavoro descrive UNITOR, uno dei sistemi partecipanti allo Stance Detection in Italian tweet (SardiStance) task. UNITOR implementa un’architettura neurale basata su Transformer, la cui accuratezza viene migliorata applicando un metodo di Transfer Learning, che sfrutta le informazioni di tre task ausiliari, ovvero Sentiment Detection, Hate Speech Detection e Irony Detection. Inoltre, l’addestramento di UNITOR puó contare su un insieme di dati scaricati ed etichettati automaticamente applicando un semplice metodo di Distant Supervision. Il sistema si é classificato al primo posto nella competizione, confermando l’efficacia delle architetture basate su Transformer e il contributo delle strategie","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131011621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4000/BOOKS.AACCADEMIA.7014
Mariano Jason Rodriguez Cisnero, Reynier Ortega Bueno
English. This document describes our participation in the Hate Speech Detection task at Evalita 2020. Our system is based on deep learning techniques, specifically RNNs and attention mechanism, mixed with transformer representations and linguistic features. In the training process a multi task learning was used to increase the system effectiveness. The results show how some of the selected features were not a good combination within the model. Nevertheless, the generalization level achieved yield encourage results.
{"title":"UO @ HaSpeeDe2: Ensemble Model for Italian Hate Speech Detection (short paper)","authors":"Mariano Jason Rodriguez Cisnero, Reynier Ortega Bueno","doi":"10.4000/BOOKS.AACCADEMIA.7014","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7014","url":null,"abstract":"English. This document describes our participation in the Hate Speech Detection task at Evalita 2020. Our system is based on deep learning techniques, specifically RNNs and attention mechanism, mixed with transformer representations and linguistic features. In the training process a multi task learning was used to increase the system effectiveness. The results show how some of the selected features were not a good combination within the model. Nevertheless, the generalization level achieved yield encourage results.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4000/BOOKS.AACCADEMIA.7270
María Fernanda Artigas Herold, Daniel Castro-Castro
This paper describes the proposal presented in the TAG-it author profiling task from EVALITA 2020 for sub-task 1. The main objective is to predict gender and age of some blog users by their posts, as well as topic they wrote about. Our proposal uses an ensemble of machine learning algorithms with three of the most used classifiers and language model of the n-grams of characters represented in a Bag of Word. To face this task we presented two different strategies aimed at finding the best possible results.
{"title":"UO_4to @ TAG-it 2020: Ensemble of Machine Learning Methods (short paper)","authors":"María Fernanda Artigas Herold, Daniel Castro-Castro","doi":"10.4000/BOOKS.AACCADEMIA.7270","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7270","url":null,"abstract":"This paper describes the proposal presented in the TAG-it author profiling task from EVALITA 2020 for sub-task 1. The main objective is to predict gender and age of some blog users by their posts, as well as topic they wrote about. Our proposal uses an ensemble of machine learning algorithms with three of the most used classifiers and language model of the n-grams of characters represented in a Bag of Word. To face this task we presented two different strategies aimed at finding the best possible results.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131550968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：