UNITOR @ Sardistance2020:结合基于变压器的架构和迁移学习进行稳健的姿态检测

Simone Giorgioni, Marcello Politi, Samir Salman, R. Basili, D. Croce
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引用次数: 14

摘要

英语。本文描述了在EVALITA 2020背景下参与意大利语推文姿态检测(Sardistance)任务的UNITOR系统。UNITOR实现了一个基于变压器的体系结构,通过采用迁移学习技术提高了其准确性。特别地,这项工作研究了与姿态检测相关的三个辅助任务的可能贡献,即情感检测,仇恨言论检测和讽刺检测。此外,UNITOR依赖于通过远程监督自动下载和标记的额外数据集。UNITOR系统在竞赛中获得Task A第一名。这证实了基于transformer的架构的有效性以及所采用策略的有益影响。意大利语。描述UNITOR, undei系统参与了允许姿态检测的意大利语推特(SardiStance)任务。UNITOR实现了基于Transformer的神经网络架构,基于迁移学习(Transfer Learning)的迁移学习(Transfer Learning),基于迁移学习(Transfer Learning)的迁移学习(Transfer Learning),基于迁移学习(Transfer Learning)的迁移学习(Transfer Learning),基于迁移学习(transvero Sentiment Detection)的迁移学习(transvero Sentiment Detection),仇恨语音检测(Hate Speech Detection)和反语检测(Irony Detection)。因此,对UNITOR puó的管理包含了对远程监控的管理数据、自动化应用程序的管理数据和远程监控的管理方法。我的系统是一个经典的、最具竞争力的、最具效率的、以变压器为基础的、最具竞争力的系统,这将有助于我们的战略
本文章由计算机程序翻译,如有差异,请以英文原文为准。
UNITOR @ Sardistance2020: Combining Transformer-based Architectures and Transfer Learning for Robust Stance Detection
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
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