论文标题
Emotiongif-iitp-ainlpml：基于合奏的自动化深度神经系统，用于预测GIF响应的类别（IES）
EmotionGIF-IITP-AINLPML: Ensemble-based Automated Deep Neural System for predicting category(ies) of a GIF response
论文作者
论文摘要
在本文中，我们将IITP-AINLPML团队提交的系统在2020年社交NLP 2020年共同任务中提交的系统，以预测给定未标记的推文的GIF响应类别（IES）。对于任务的第1轮阶段，我们提出了一个基于注意力的双向GRU网络，该网络对Tweet（文本）及其答复（如可用的文本）和给定类别（IES）以及其GIF响应进行了培训。在第二轮阶段，我们为任务构建了几个基于神经的分类器，并通过以多数投票的合奏技术来报告最终预测。我们提出的模型分别获得第1轮和第2轮的最佳平均召回（MR）分别为52.92％和53.80％。
In this paper, we describe the systems submitted by our IITP-AINLPML team in the shared task of SocialNLP 2020, EmotionGIF 2020, on predicting the category(ies) of a GIF response for a given unlabelled tweet. For the round 1 phase of the task, we propose an attention-based Bi-directional GRU network trained on both the tweet (text) and their replies (text wherever available) and the given category(ies) for its GIF response. In the round 2 phase, we build several deep neural-based classifiers for the task and report the final predictions through a majority voting based ensemble technique. Our proposed models attain the best Mean Recall (MR) scores of 52.92% and 53.80% in round 1 and round 2, respectively.
