NIT-Agartala-NLP-Team at SemEval-2020 Task 8: Building Multimodal Classifiers to tackle Internet Humor
This work addresses the challenge of multimodal humor classification for NLP researchers, but it is incremental as it applies existing methods like BERT and BiLSTM to a new dataset.
The paper tackled the problem of classifying internet humor in memes by building multimodal classifiers for SemEval-2020 Task 8, achieving ranks of 24/33, 11/29, and 15/26 across three sub-tasks.
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.