论文标题
一个基于BERT和LIGHTGBM的混合模型，用于预测Twitter上的情绪GIF类别
A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter
论文作者
论文摘要
动画图形互换格式（GIF）图像已在社交媒体上广泛使用，作为一种直观的表达方式。鉴于它们的表现力，GIF提供了一种更细微和精确的方式来传达情绪。在本文中，我们介绍了2020年EmotionGif 2020挑战的解决方案，即2020年社交NLP的共同任务。为了推荐GIF类别未标记的推文，我们将此问题视为一种匹配的任务，并提出了一个学习来基于BiveRectional Engoder表示的学习框架（BERT）和LightGBM。我们的团队在第1轮排行榜上以平均平均精度为6（6）得分为0.5394。
The animated Graphical Interchange Format (GIF) images have been widely used on social media as an intuitive way of expression emotion. Given their expressiveness, GIFs offer a more nuanced and precise way to convey emotions. In this paper, we present our solution for the EmotionGIF 2020 challenge, the shared task of SocialNLP 2020. To recommend GIF categories for unlabeled tweets, we regarded this problem as a kind of matching tasks and proposed a learning to rank framework based on Bidirectional Encoder Representations from Transformer (BERT) and LightGBM. Our team won the 4th place with a Mean Average Precision @ 6 (MAP@6) score of 0.5394 on the round 1 leaderboard.
