By1510 @ HaSpeeDe 2: Identification of Hate Speech for Italian Language in Social Media Data (short paper)

T. Deng, Yang Bai, Hongbing Dai
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引用次数: 1

Abstract

English. Hate speech detection has become a crucial mission in many fields. This paper introduces the system of team By1510. In this work, we participate in the HaSpeeDe 2 (Hate Speech Detection) shared task which is organized within Evalita 2020(The Final Workshop of the 7th evaluation campaign). In order to obtain more abundant semantic information, we combine the original output of BERT-Ita and the hidden state outputs of BERT-Ita. We take part in task A. Our model achieves an F1 score of 77.66% (6/27) in the tweets test set and our model achieves an F1 score of 66.38% (14/27) in the news headlines test set. Italiano. L’ individuazione dell’ incitamento allodio diventata una missione cruciale in molti campi. Questo articolo introduce il sistema del team By1510. In questo lavoro, partecipiamo al task HaSpeeDe 2 che stato organizzato allinterno di Evalita 2020. Per ottenere informazioni semantiche pi abbondanti abbiamo combinato loutput originale di BERT Ita e gli output di hidden state di BERT Ita. Il sistema presentato partecipa al task A. Il nostro modello ottiene un punteggio F1 di 77.66% (6/27) sui dati di test da Twitter e un punteggio F1 di 66.38% (14/27) sui dati di test contenenti titoli di quotidiano. Copyright c © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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By1510 @ HaSpeeDe 2:社交媒体数据中意大利语仇恨言论的识别(短文)
英语。仇恨言论检测已成为许多领域的一项重要任务。本文介绍了团队By1510系统。在这项工作中,我们参与了在Evalita 2020(第七次评估活动的最终研讨会)中组织的HaSpeeDe 2(仇恨言论检测)共享任务。为了获得更丰富的语义信息,我们将BERT-Ita的原始输出和BERT-Ita的隐藏状态输出结合起来。我们参加任务a。我们的模型在tweets测试集中获得了77.66%(6/27)的F1分数,在news headlines测试集中获得了66.38%(14/27)的F1分数。意大利语。我的“个性”和“激励”都是为了实现我们的使命。Questo articolo介绍了il系统模型团队By1510。为了解决这个问题,参与性任务将在2020年的所有评估期间组织起来。根据不同的信息语义,将abbond和abbiamo相结合,输出原始状态的BERT - Ita和隐藏状态的BERT - Ita。i nostro modelello ottiene un punteggio F1 di 77.66% (6/27) sui dati di test da Twitter e un punteggio F1 di 66.38% (14/27) sui dati di test contententi titoli di quotidiano。本文版权所有c©2020。在知识共享许可国际署名4.0 (CC BY 4.0)下允许使用。
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DIACR-Ita @ EVALITA2020: Overview of the EVALITA2020 Diachronic Lexical Semantics (DIACR-Ita) Task QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian (short paper) By1510 @ HaSpeeDe 2: Identification of Hate Speech for Italian Language in Social Media Data (short paper) HaSpeeDe 2 @ EVALITA2020: Overview of the EVALITA 2020 Hate Speech Detection Task KIPoS @ EVALITA2020: Overview of the Task on KIParla Part of Speech Tagging
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