Pub Date : 1900-01-01DOI: 10.4000/BOOKS.AACCADEMIA.7420
Claudia Breazzano, E. Rubino, D. Croce, R. Basili
This paper describes the UNITOR system that participated to the “multimoDal Artefacts recogNition Knowledge for MEMES” (DANKMEMES) task within the context of EVALITA 2020. UNITOR implements a neural model which combines a Deep Convolutional Neural Network to encode visual information of input images and a Transformerbased architecture to encode the meaning of the attached texts. UNITOR ranked first in all subtasks, clearly confirming the robustness of the investigated neural architectures and suggesting the beneficial impact of the proposed combination strategy.
{"title":"UNITOR @ DANKMEME: Combining Convolutional Models and Transformer-based architectures for accurate MEME management","authors":"Claudia Breazzano, E. Rubino, D. Croce, R. Basili","doi":"10.4000/BOOKS.AACCADEMIA.7420","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7420","url":null,"abstract":"This paper describes the UNITOR system that participated to the “multimoDal Artefacts recogNition Knowledge for MEMES” (DANKMEMES) task within the context of EVALITA 2020. UNITOR implements a neural model which combines a Deep Convolutional Neural Network to encode visual information of input images and a Transformerbased architecture to encode the meaning of the attached texts. UNITOR ranked first in all subtasks, clearly confirming the robustness of the investigated neural architectures and suggesting the beneficial impact of the proposed combination strategy.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"36 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":"127682060","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.6807
Giuseppe Attanasio, Eliana Pastor
We present a multi-agent classification solution for identifying misogynous and aggressive content in Italian tweets. A first agent uses modern Sentence Embedding techniques to encode tweets and a SVM classifier to produce initial labels. A second agent, based on TF-IDF and Misogyny Italian lexicons, is jointly adopted to improve the first agent on uncertain predictions. We evaluate our approach in the Automatic Misogyny Identification Shared Task of the EVALITA 2020 campaign. Results show that TF-IDF and lexicons effectively improve the supervised agent trained on sentence embeddings. Italiano. Presentiamo un classificatore multi-agente per identificare tweet italiani misogini e aggressivi. Un primo agente codifica i tweet con Sentence Embedding e una SVM per produrre le etichette iniziali. Un secondo agente, basato su TF-IDF e lessici misogini, è usato per coadiuvare il primo agente nelle predizioni incerte. Applichiamo la soluzione al task AMI della campagna EVALITA 2020. I risultati mostrano che TF-IDF e i lessici migliorano le performance del primo agente addestrato su sentence embedding.
我们提出了一个多智能体分类解决方案，用于识别意大利语推文中的厌女和攻击性内容。第一智能体使用现代句子嵌入技术对tweet进行编码，并使用支持向量机分类器生成初始标签。基于TF-IDF和Misogyny意大利语词汇的第二个代理被联合采用，以改进第一个代理对不确定预测的处理。我们在EVALITA 2020运动的自动厌女症识别共享任务中评估了我们的方法。结果表明，TF-IDF和词典有效地改善了句子嵌入训练的监督智能体。意大利语。呈现一种非分类的、多代理的、每条身份推文的意大利式厌女攻击。利用支持向量机对推文和句子嵌入的初始化问题进行求解。第二剂，basato su TF-IDF，较弱的misogini， è usato per codiuva，第一剂，较弱的预测。应用解决方案的所有任务AMI della campagna EVALITA 2020。结果表明，TF-IDF算法在句子嵌入中具有较低的性能和较低的性能。
{"title":"PoliTeam @ AMI: Improving Sentence Embedding Similarity with Misogyny Lexicons for Automatic Misogyny Identification in Italian Tweets","authors":"Giuseppe Attanasio, Eliana Pastor","doi":"10.4000/BOOKS.AACCADEMIA.6807","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.6807","url":null,"abstract":"We present a multi-agent classification solution for identifying misogynous and aggressive content in Italian tweets. A first agent uses modern Sentence Embedding techniques to encode tweets and a SVM classifier to produce initial labels. A second agent, based on TF-IDF and Misogyny Italian lexicons, is jointly adopted to improve the first agent on uncertain predictions. We evaluate our approach in the Automatic Misogyny Identification Shared Task of the EVALITA 2020 campaign. Results show that TF-IDF and lexicons effectively improve the supervised agent trained on sentence embeddings. Italiano. Presentiamo un classificatore multi-agente per identificare tweet italiani misogini e aggressivi. Un primo agente codifica i tweet con Sentence Embedding e una SVM per produrre le etichette iniziali. Un secondo agente, basato su TF-IDF e lessici misogini, è usato per coadiuvare il primo agente nelle predizioni incerte. Applichiamo la soluzione al task AMI della campagna EVALITA 2020. I risultati mostrano che TF-IDF e i lessici migliorano le performance del primo agente addestrato su sentence embedding.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"74 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":"115946515","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.6979
Michele Fontana, Giuseppe Attardi
We describe our approach and experiments to tackle Task A of the second edition of HaSpeeDe, within the Evalita 2020 evaluation campaign. The proposed model consists in an ensemble of classifiers built from three variants of a common neural architecture. Each classifier uses contextual representations from transformers trained on Italian texts, fine tuned on the training set of the challenge. We tested the proposed model on the two official test sets, the in-domain test set containing just tweets and the out-of-domain one including also news headlines. Our submissions ranked 4th on the tweets test set and 17th on the second test set.
{"title":"Fontana-Unipi @ HaSpeeDe2: Ensemble of transformers for the Hate Speech task at Evalita (short paper)","authors":"Michele Fontana, Giuseppe Attardi","doi":"10.4000/BOOKS.AACCADEMIA.6979","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.6979","url":null,"abstract":"We describe our approach and experiments to tackle Task A of the second edition of HaSpeeDe, within the Evalita 2020 evaluation campaign. The proposed model consists in an ensemble of classifiers built from three variants of a common neural architecture. Each classifier uses contextual representations from transformers trained on Italian texts, fine tuned on the training set of the challenge. We tested the proposed model on the two official test sets, the in-domain test set containing just tweets and the out-of-domain one including also news headlines. Our submissions ranked 4th on the tweets test set and 17th on the second test set.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"50 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":"131225876","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}
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