Multi-resolution Annotations for Emoji Prediction
Weicheng Ma, Ruibo Liu, Lili Wang, Soroush Vosoughi · 2020
Emojis are able to express various linguistic components, including emotions, sentiments, events, etc. Predicting the proper emojis associated with text provides a way to summarize the text accurately, and it has been proven to be a good auxiliary task to many Natural Language Understanding (NLU) tasks.Labels in existing emoji prediction datasets are all passage-based and are usually under the multi-class classification setting.However, in many cases, one single emoji cannot fully cover the theme of a piece of text.It is thus useful to infer the part of text related to each emoji.The lack of multi-label and aspectlevel emoji prediction datasets is one of the bottlenecks for this task.This paper annotates an emoji prediction dataset with passage-level multi-class/multi-label, and aspect-level multiclass annotations.We also present a novel annotation method with which we generate the aspect-level annotations.The annotations are generated heuristically, taking advantage of the self-attention mechanism in Transformer networks.We validate the annotations both automatically and manually to ensure their quality.We also benchmark the dataset with a pretrained BERT model.