Pub Date : 1900-01-01DOI: 10.4000/BOOKS.AACCADEMIA.7780
Thomas Proisl, Gabriella Lapesa
In this paper, we describe experiments on part-of-speech tagging of spoken Italian that we conducted in the context of the EVALITA 2020 KIPoS shared task (Bosco et al., 2020). Our submission to the shared task is based on SoMeWeTa (Proisl, 2018), a tagger which supports domain adaptation and is designed to flexibly incorporate external resources. We document our approach and discuss our results in the shared task along with a statistical analysis of the factors which impact performance the most. Additionally, we report on a set of additional experiments involving the combination of neural language models with unsupervised HMMs, and compare its performance to that of our system.
在本文中，我们描述了我们在EVALITA 2020 KIPoS共享任务(Bosco et al.， 2020)的背景下进行的意大利语口语词性标注实验。我们提交的共享任务是基于SoMeWeTa (Proisl, 2018)，这是一个支持领域自适应的标记器，旨在灵活地整合外部资源。我们记录我们的方法，并在共享任务中讨论我们的结果，同时对影响性能最大的因素进行统计分析。此外，我们报告了一组涉及神经语言模型与无监督hmm相结合的附加实验，并将其性能与我们的系统进行了比较。
{"title":"KLUMSy @ KIPoS: Experiments on Part-of-Speech Tagging of Spoken Italian","authors":"Thomas Proisl, Gabriella Lapesa","doi":"10.4000/BOOKS.AACCADEMIA.7780","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7780","url":null,"abstract":"In this paper, we describe experiments on part-of-speech tagging of spoken Italian that we conducted in the context of the EVALITA 2020 KIPoS shared task (Bosco et al., 2020). Our submission to the shared task is based on SoMeWeTa (Proisl, 2018), a tagger which supports domain adaptation and is designed to flexibly incorporate external resources. We document our approach and discuss our results in the shared task along with a statistical analysis of the factors which impact performance the most. Additionally, we report on a set of additional experiments involving the combination of neural language models with unsupervised HMMs, and compare its performance to that of our system.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"19 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":"131931624","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.6764
E. Fersini, Debora Nozza, Paolo Rosso
English. Automatic Misogyny Identification (AMI) is a shared task proposed at the Evalita 2020 evaluation campaign. The AMI challenge, based on Italian tweets, is organized into two subtasks: (1) Subtask A about misogyny and aggressiveness identification and (2) Subtask B about the fairness of the model. At the end of the evaluation phase, we received a total of 20 runs for Subtask A and 11 runs for Subtask B, submitted by 8 teams. In this paper, we present an overview of the AMI shared task, the datasets, the evaluation method-ology, the results obtained by the participants and a discussion about the method-ology adopted by the teams. Finally, we draw some conclusions and discuss future work.
{"title":"AMI @ EVALITA2020: Automatic Misogyny Identification","authors":"E. Fersini, Debora Nozza, Paolo Rosso","doi":"10.4000/BOOKS.AACCADEMIA.6764","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.6764","url":null,"abstract":"English. Automatic Misogyny Identification (AMI) is a shared task proposed at the Evalita 2020 evaluation campaign. The AMI challenge, based on Italian tweets, is organized into two subtasks: (1) Subtask A about misogyny and aggressiveness identification and (2) Subtask B about the fairness of the model. At the end of the evaluation phase, we received a total of 20 runs for Subtask A and 11 runs for Subtask B, submitted by 8 teams. In this paper, we present an overview of the AMI shared task, the datasets, the evaluation method-ology, the results obtained by the participants and a discussion about the method-ology adopted by the teams. Finally, we draw some conclusions and discuss future work.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"41 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":"132590575","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.6789
Alyssa Lees, Jeffrey Scott Sorensen, I. Kivlichan
The Google Jigsaw team produced submissions for two of the EVALITA 2020 (Basile et al., 2020) shared tasks, based in part on the technology that powers the publicly available PerspectiveAPI comment evaluation service. We present a basic description of our submitted results and a review of the types of errors that our system made in these shared tasks.
{"title":"Jigsaw @ AMI and HaSpeeDe2: Fine-Tuning a Pre-Trained Comment-Domain BERT Model","authors":"Alyssa Lees, Jeffrey Scott Sorensen, I. Kivlichan","doi":"10.4000/BOOKS.AACCADEMIA.6789","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.6789","url":null,"abstract":"The Google Jigsaw team produced submissions for two of the EVALITA 2020 (Basile et al., 2020) shared tasks, based in part on the technology that powers the publicly available PerspectiveAPI comment evaluation service. We present a basic description of our submitted results and a review of the types of errors that our system made in these shared tasks.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"2676 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":"127487255","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.6782
Samuel Fabrizi
The presence of misogynistic contents is one of the most crucial problems of social networks. In this paper we present our system for misogyny identification on Twitter. Our approach is based on a convolutional neural network that exploits pretrained word embeddings. We also experimented a comparison among different architectures to understand the effectiveness of our method. The paper also described our submissions to both subtasks A and B to Automatic Misogyny Identification competition at Evalita 2020.
{"title":"fabsam @ AMI: A Convolutional Neural Network Approach (short paper)","authors":"Samuel Fabrizi","doi":"10.4000/BOOKS.AACCADEMIA.6782","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.6782","url":null,"abstract":"The presence of misogynistic contents is one of the most crucial problems of social networks. In this paper we present our system for misogyny identification on Twitter. Our approach is based on a convolutional neural network that exploits pretrained word embeddings. We also experimented a comparison among different architectures to understand the effectiveness of our method. The paper also described our submissions to both subtasks A and B to Automatic Misogyny Identification competition at Evalita 2020.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"63 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":"132594030","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.6769
Arianna Muti, Alberto Barrón-Cedeño
We describe our participation in the EVALITA 2020 (Basile et al., 2020) shared task on Automatic Misogyny Identification. We focus on task A —Misogyny and Aggressive Behaviour Identification— which aims at detecting whether a tweet in Italian is misogynous and, if so, whether it is aggressive. Rather than building two different models, one for misogyny and one for aggressiveness identification, we handle the problem as one single multi-label classification task, considering three classes: nonmisogynous, non-aggressive misogynous, and aggressive misogynous. Our threeclass supervised model, built on top of AlBERTo, obtains an overall F1 score of 0.7438 on the task test set (F1 = 0.8102 for the misogyny and F1 = 0.6774 for the aggressiveness task), which outperforms the top submitted model (F1 = 0.7406).1
{"title":"UniBO @ AMI: A Multi-Class Approach to Misogyny and Aggressiveness Identification on Twitter Posts Using AlBERTo","authors":"Arianna Muti, Alberto Barrón-Cedeño","doi":"10.4000/BOOKS.AACCADEMIA.6769","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.6769","url":null,"abstract":"We describe our participation in the EVALITA 2020 (Basile et al., 2020) shared task on Automatic Misogyny Identification. We focus on task A —Misogyny and Aggressive Behaviour Identification— which aims at detecting whether a tweet in Italian is misogynous and, if so, whether it is aggressive. Rather than building two different models, one for misogyny and one for aggressiveness identification, we handle the problem as one single multi-label classification task, considering three classes: nonmisogynous, non-aggressive misogynous, and aggressive misogynous. Our threeclass supervised model, built on top of AlBERTo, obtains an overall F1 score of 0.7438 on the task test set (F1 = 0.8102 for the misogyny and F1 = 0.6774 for the aggressiveness task), which outperforms the top submitted model (F1 = 0.7406).1","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"76 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":"115180762","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.7498
Federico Sangati, Antonio Pascucci, J. Monti
English. This paper describes Il mago della Ghigliottina , a bot which took part in the Ghigliottin-AI task of the Evalita 2020 evaluation campaign. The aim is to build a system able to solve the TV game “La Ghigliottina”. Our system has already participated in the Evalita 2018 task NLP4FUN . Compared to that occasion, it improved its accuracy from 61% to 68.6%. Italiano. Questo contributo descrive Il mago della Ghigliottina, un bot che ha partecipato a Ghigliottin-AI, uno dei task di Evalita 2020. Scopo del task è mettere in piedi un sistema automatico capace di risolvere il gioco televisivo “La Ghigliot-tina”. Il nostro sistema ha già parteci-pato all’edizione del 2018 di Evalita al task NLP4FUN. Rispetto all’edizione del 2018 di NLP4FUN, l’accuratezza è salita dal 61% al 68.6%.
{"title":"\"Il Mago della Ghigliottina\" @ GhigliottinAI: When Linguistics meets Artificial Intelligence","authors":"Federico Sangati, Antonio Pascucci, J. Monti","doi":"10.4000/BOOKS.AACCADEMIA.7498","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7498","url":null,"abstract":"English. This paper describes Il mago della Ghigliottina , a bot which took part in the Ghigliottin-AI task of the Evalita 2020 evaluation campaign. The aim is to build a system able to solve the TV game “La Ghigliottina”. Our system has already participated in the Evalita 2018 task NLP4FUN . Compared to that occasion, it improved its accuracy from 61% to 68.6%. Italiano. Questo contributo descrive Il mago della Ghigliottina, un bot che ha partecipato a Ghigliottin-AI, uno dei task di Evalita 2020. Scopo del task è mettere in piedi un sistema automatico capace di risolvere il gioco televisivo “La Ghigliot-tina”. Il nostro sistema ha già parteci-pato all’edizione del 2018 di Evalita al task NLP4FUN. Rispetto all’edizione del 2018 di NLP4FUN, l’accuratezza è salita dal 61% al 68.6%.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"1 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":"122440107","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.7027
Adriano dos S. R. da Silva, N. T. Roman
English. In this article, we present the results of applying a Stacking Ensemble method to the problem of hate speech classification proposed in the main task of HaSpeeDe 2 at EVALITA 2020. The model was then compared to a Logistic Regression classifier, along with two other benchmarks defined by the competition’s organising committee (an SVM with a linear kernel and a majority class classifier). Results showed our Ensemble to outperform the benchmarks to various degrees, both when testing in the same domain as training and in a different domain. Italiano. In questo articolo, ci presentiamo i risultati dell’applicazione di un modello di Stacking Ensemble al problema della classificazione dei discorsi di incitamento all’odio nel compito A di EVALITA (HaSpeeDe 2). Il modello è stato quindi confrontato con un modello di regressione logistica, insieme ad altri due benchmark definiti dal comitato organizzatore della competizione (un SVM con un kernel lineare e un classificatore di classe maggioritaria). I risultati hanno mostrato che il nostro Ensemble supera i benchmark a vari livelli, sia durante i test nello stesso dominio di sviluppo che in un dominio di-
{"title":"No Place For Hate Speech @ HaSpeeDe 2: Ensemble to Identify Hate Speech in Italian (short paper)","authors":"Adriano dos S. R. da Silva, N. T. Roman","doi":"10.4000/BOOKS.AACCADEMIA.7027","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7027","url":null,"abstract":"English. In this article, we present the results of applying a Stacking Ensemble method to the problem of hate speech classification proposed in the main task of HaSpeeDe 2 at EVALITA 2020. The model was then compared to a Logistic Regression classifier, along with two other benchmarks defined by the competition’s organising committee (an SVM with a linear kernel and a majority class classifier). Results showed our Ensemble to outperform the benchmarks to various degrees, both when testing in the same domain as training and in a different domain. Italiano. In questo articolo, ci presentiamo i risultati dell’applicazione di un modello di Stacking Ensemble al problema della classificazione dei discorsi di incitamento all’odio nel compito A di EVALITA (HaSpeeDe 2). Il modello è stato quindi confrontato con un modello di regressione logistica, insieme ad altri due benchmark definiti dal comitato organizzatore della competizione (un SVM con un kernel lineare e un classificatore di classe maggioritaria). I risultati hanno mostrato che il nostro Ensemble supera i benchmark a vari livelli, sia durante i test nello stesso dominio di sviluppo che in un dominio di-","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"316 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120871381","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.6989
Eric Lavergne, Rajkumar Saini, György Kovács, Killian Murphy
English. This report was written to describe the systems that were submitted by the team “TheNorth” for the HaSpeeDe 2 shared task organised within EVALITA 2020. To address the main task which is hate speech detection, we fine-tuned BERT-based models. We evaluated both multilingual and Italian language models trained with the data provided and additional data. We also studied the contributions of multitask learning considering both hate speech detection and stereotype detection tasks.
{"title":"TheNorth @ HaSpeeDe 2: BERT-based Language Model Fine-tuning for Italian Hate Speech Detection (short paper)","authors":"Eric Lavergne, Rajkumar Saini, György Kovács, Killian Murphy","doi":"10.4000/BOOKS.AACCADEMIA.6989","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.6989","url":null,"abstract":"English. This report was written to describe the systems that were submitted by the team “TheNorth” for the HaSpeeDe 2 shared task organised within EVALITA 2020. To address the main task which is hate speech detection, we fine-tuned BERT-based models. We evaluated both multilingual and Italian language models trained with the data provided and additional data. We also studied the contributions of multitask learning considering both hate speech detection and stereotype detection tasks.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"10 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":"133029326","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.7360
G. Vlad, George-Eduard Zaharia, Dumitru-Clementin Cercel, M. Dascalu
Certain events or political situations determine users from the online environment to express themselves by using different modalities. One of them is represented by Internet memes, which combine text with a representative image to entail a wide range of emotions, from humor to sarcasm and even hate. In this paper, we describe our approach for the DANKMEMES competition from EVALITA 2020 consisting of a multimodal multi-task learning architecture based on two main components. The first one is a Graph Convolutional Network combined with an Italian BERT for text encoding, while the second is varied between different image-based architectures (i.e., ResNet50, ResNet152, and VGG-16) for image representation. Our solution achieves good performance on the first two tasks of the current competition, ranking 3rd for both Task 1 (.8437 macroF1 score) and Task 2 (.8169 macro-F1 score), while exceeding by high margins the official baselines.
{"title":"UPB @ DANKMEMES: Italian Memes Analysis - Employing Visual Models and Graph Convolutional Networks for Meme Identification and Hate Speech Detection (short paper)","authors":"G. Vlad, George-Eduard Zaharia, Dumitru-Clementin Cercel, M. Dascalu","doi":"10.4000/BOOKS.AACCADEMIA.7360","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7360","url":null,"abstract":"Certain events or political situations determine users from the online environment to express themselves by using different modalities. One of them is represented by Internet memes, which combine text with a representative image to entail a wide range of emotions, from humor to sarcasm and even hate. In this paper, we describe our approach for the DANKMEMES competition from EVALITA 2020 consisting of a multimodal multi-task learning architecture based on two main components. The first one is a Graph Convolutional Network combined with an Italian BERT for text encoding, while the second is varied between different image-based architectures (i.e., ResNet50, ResNet152, and VGG-16) for image representation. Our solution achieves good performance on the first two tasks of the current competition, ranking 3rd for both Task 1 (.8437 macroF1 score) and Task 2 (.8169 macro-F1 score), while exceeding by high margins the official baselines.","PeriodicalId":184564,"journal":{"name":"EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020","volume":"1 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":"129114313","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.7152
Federico Ferraccioli, Andrea Sciandra, Mattia Da Pont, P. Girardi, Dario Solari, L. Finos
In this contribution we describe the system (i.e. a statistical model) used to participate in Evalita conference 2020, SardiStance (Tasks A and B) and Haspeede2 (Tasks A and B). We first developed a classifier by extracting features from the texts and the social network of users. Then, we fit the data through an extreme gradient boosting, with cross-validation tuning of the hyper-parameters. A key factor for a good performance in SardiStance Task B was the features extraction by using Multidimensional Scaling of the distance matrix (minimum path, undirected graph) applied on each network. The second system exploits the same features above, but it trains and performs predictions in twosteps. The performances proved to be lower than those of the single-step model.
{"title":"TextWiller @ SardiStance, HaSpeede2: Text or Con-text? A Smart Use of Social Network Data in Predicting Polarization (short paper)","authors":"Federico Ferraccioli, Andrea Sciandra, Mattia Da Pont, P. Girardi, Dario Solari, L. Finos","doi":"10.4000/BOOKS.AACCADEMIA.7152","DOIUrl":"https://doi.org/10.4000/BOOKS.AACCADEMIA.7152","url":null,"abstract":"In this contribution we describe the system (i.e. a statistical model) used to participate in Evalita conference 2020, SardiStance (Tasks A and B) and Haspeede2 (Tasks A and B). We first developed a classifier by extracting features from the texts and the social network of users. Then, we fit the data through an extreme gradient boosting, with cross-validation tuning of the hyper-parameters. A key factor for a good performance in SardiStance Task B was the features extraction by using Multidimensional Scaling of the distance matrix (minimum path, undirected graph) applied on each network. The second system exploits the same features above, but it trains and performs predictions in twosteps. The performances proved to be lower than those of the single-step model.","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":"123682179","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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