Interpretable Emoji Prediction via Label-Wise Attention LSTMs
Human language has evolved towards newer forms of communication such as social media, where emojis (i.e., ideograms bearing a visual meaning) play a key role. While there is an increasing body of work aimed at the computational modeling of emoji semantics, there is currently little understanding about what makes a computational model represent or predict a given emoji in a certain way. In this paper we propose a label-wise attention mechanism with which we attempt to better understand the nuances underlying emoji prediction. In addition to advantages in terms of interpretability, we show that our proposed architecture improves over standard baselines in emoji prediction, and does particularly well when predicting infrequent emojis.
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Emotion RecognitionInformation RetrievalLanguage ModelingLanguage ModellingMachine TranslationPredictionSentiment AnalysisSimilar Papers 제목 키워드 기반
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