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Interpretable Emoji prediction via label-wise attention LSTM's

Barbieri, Francesco, Espinosa Anke, Luis, Camacho-Collados, Jose, Schockaert, Steven ORCID: and Saggion, Horacio 2018. Interpretable Emoji prediction via label-wise attention LSTM's. Presented at: Conference on Empirical Methods in Natural Language Processing, Brussels, 31 October-4 November.

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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.

Item Type: Conference or Workshop Item (Paper)
Status: Unpublished
Schools: Computer Science & Informatics
Last Modified: 24 Oct 2022 07:23

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