Barbieri, Francesco, Espinosa Anke, Luis, Camacho-Collados, Jose, Schockaert, Steven  ORCID: https://orcid.org/0000-0002-9256-2881 and Saggion, Horacio
2018.
Interpretable Emoji prediction via label-wise attention LSTM's.
Presented at: Conference on Empirical Methods in Natural Language Processing,
Brussels, Belgium,
31 October-4 November.
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.
Association for Computational Linguistics,
pp. 4766-4771.
10.18653/v1/D18-1508
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Abstract
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 - published (Paper) |
|---|---|
| Date Type: | Publication |
| Status: | Published |
| Schools: | Schools > Computer Science & Informatics |
| Publisher: | Association for Computational Linguistics |
| ISBN: | 9781948087841 |
| Date of First Compliant Deposit: | 20 May 2026 |
| Last Modified: | 20 May 2026 11:19 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/114794 |
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