Interpretable emoji prediction via label-wise attention LSTMs

Abstract

Comunicació presentada a la Conference on Empirical Methods in Natural Language Processing, celebrada del 31 d'octubre al 4 de novembre de 2018 a Brussel·les, Bèlgica.

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.

F. Barbieri and H. Saggion acknowledge support from the TUNER project (TIN2015-65308-C5-5- R, MINECO/FEDER, UE). Luis Espinosa-Anke, Jose Camacho-Collados and Steven Schockaert have been supported by ERC Starting Grant 637277.