@inproceedings{barbieri-etal-2018-interpretable,
title = "Interpretable Emoji Prediction via Label-Wise Attention {LSTM}s",
author = "Barbieri, Francesco and
Espinosa-Anke, Luis and
Camacho-Collados, Jose and
Schockaert, Steven and
Saggion, Horacio",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1508/",
doi = "10.18653/v1/D18-1508",
pages = "4766--4771",
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."
}
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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.</abstract>
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%0 Conference Proceedings
%T Interpretable Emoji Prediction via Label-Wise Attention LSTMs
%A Barbieri, Francesco
%A Espinosa-Anke, Luis
%A Camacho-Collados, Jose
%A Schockaert, Steven
%A Saggion, Horacio
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F barbieri-etal-2018-interpretable
%X 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.
%R 10.18653/v1/D18-1508
%U https://aclanthology.org/D18-1508/
%U https://doi.org/10.18653/v1/D18-1508
%P 4766-4771
Markdown (Informal)
[Interpretable Emoji Prediction via Label-Wise Attention LSTMs](https://aclanthology.org/D18-1508/) (Barbieri et al., EMNLP 2018)
ACL
- Francesco Barbieri, Luis Espinosa-Anke, Jose Camacho-Collados, Steven Schockaert, and Horacio Saggion. 2018. Interpretable Emoji Prediction via Label-Wise Attention LSTMs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4766–4771, Brussels, Belgium. Association for Computational Linguistics.