论文标题
Semeval-2020的Neuro Team Neuro任务8：使用多任务学习对模因进行多模式的细粒情绪分类
Team Neuro at SemEval-2020 Task 8: Multi-Modal Fine Grain Emotion Classification of Memes using Multitask Learning
论文作者
论文摘要
在本文中，我们描述了我们用于备忘分析挑战的系统，即Semeval-2020的任务8。这个挑战具有三个子任务，其中需要对模因的基于影响的情感分类以及强度。我们提出的系统通过将三个任务表示为多标签分层分类问题，将三个任务组合到一个任务中。此处，多任务学习或联合学习程序用于训练我们的模型。我们使用双通道来从单独的深层神经网络中提取基于文本和图像的特征，并汇总它们以创建任务特定于任务特定的功能。然后，这些特定任务的汇总特征向量韦尔将其传递给具有密集层的较小网络，每个网络都分配了一个用于预测一种类型的细粒情感标签。我们提出的方法显示了该系统在少数任务中与挑战中其他最佳模型的优越性。
In this article, we describe the system that we used for the memotion analysis challenge, which is Task 8 of SemEval-2020. This challenge had three subtasks where affect based sentiment classification of the memes was required along with intensities. The system we proposed combines the three tasks into a single one by representing it as multi-label hierarchical classification problem.Here,Multi-Task learning or Joint learning Procedure is used to train our model.We have used dual channels to extract text and image based features from separate Deep Neural Network Backbone and aggregate them to create task specific features. These task specific aggregated feature vectors ware then passed on to smaller networks with dense layers, each one assigned for predicting one type of fine grain sentiment label. Our Proposed method show the superiority of this system in few tasks to other best models from the challenge.
