论文标题
nit-agartala-nlp团队在Semeval-2020任务8：构建多模式分类器以应对互联网幽默
NIT-Agartala-NLP-Team at SemEval-2020 Task 8: Building Multimodal Classifiers to tackle Internet Humor
论文作者
论文摘要
本文描述了提交给Semeval-2020任务8的系统8：``NIT-AGARTALA-NLP-team''的纪念活动。任务组织者提供了8879模因的数据集来培训和测试我们的模型。我们的系统包括逻辑回归基线，Bilstm +基于注意力的学习者以及BERT的转移学习方法。对于三个子任务A，B和C，我们分别获得了24/33、11/29和15/26的排名。我们强调了我们在利用图像信息以及我们采用的一些技术和手工特征来克服这些问题方面的困难。我们还讨论了各种建模问题，并将可能的解决方案和原因理论化，以了解这些问题的原因。
The paper describes the systems submitted to SemEval-2020 Task 8: Memotion by the `NIT-Agartala-NLP-Team'. A dataset of 8879 memes was made available by the task organizers to train and test our models. Our systems include a Logistic Regression baseline, a BiLSTM + Attention-based learner and a transfer learning approach with BERT. For the three sub-tasks A, B and C, we attained ranks 24/33, 11/29 and 15/26, respectively. We highlight our difficulties in harnessing image information as well as some techniques and handcrafted features we employ to overcome these issues. We also discuss various modelling issues and theorize possible solutions and reasons as to why these problems persist.
