SSN NLP @ SardiStance : Stance Detection from Italian Tweets using RNN and Transformers (short paper)

S. Kayalvizhi, D. Thenmozhi, Aravindan Chandrabose
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引用次数: 1

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.
SSN NLP @ SardiStance:使用RNN和transformer从意大利语推文中进行姿态检测(短文)
立场检测是指从被测者的陈述中检测自己对被测者的看法。sardistance任务的目的是将意大利语推文分为对目标对象有好感、反对或没有好感的三类。该任务有两个子任务:在任务A中,分类必须只考虑文本含义,而在任务B中,必须考虑上下文信息和文本含义对tweet进行分类。我们已经提出了使用编码器-解码器模型和转换器仅利用文本含义检测姿态的解决方案(任务A)。在这两种方法中,简单变压器的表现优于编码器-解码器模型,平均f1得分为0.4707。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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