This paper describes our system (MIC-CIS) details and results of participation in the fine-grained propaganda detection shared task 2019. To address the tasks of sentence (SLC) and fragment level (FLC) propaganda detection, we explore different neural architectures (e.g., CNN, LSTM-CRF and BERT) and extract linguistic (e.g., part-of-speech, named entity, readability, sentiment, emotion, etc.), layout and topical features. Specifically, we have designed multi-granularity and multi-tasking neural architectures to jointly perform both the sentence and fragment level propaganda detection. Additionally, we investigate different ensemble schemes such as majority-voting, relax-voting, etc. to boost overall system performance. Compared to the other participating systems, our submissions are ranked 3rd and 4th in FLC and SLC tasks, respectively.
翻译:本文介绍我们的系统(MIC-CIS)参与精细宣传探测共同任务(2019年)的细节和结果。为了执行判决任务(SLC)和碎片层次(FLC)的宣传探测,我们探索了不同的神经结构(如CNN、LSTM-CRF和BERT),并提取语言(如部分语言、命名实体、可读性、情感、情感等)、布局和专题特征。具体地说,我们设计了多组合和多任务神经结构,以共同执行判决和碎片层次的宣传探测。此外,我们调查了不同的组合计划,如多数投票、放松投票等,以提高整个系统的业绩。与其他参与系统相比,我们提交的材料在FLC和SLC的任务中分别排在第3和第4位。