Title:
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Interpretable emoji prediction via label-wise attention LSTMs
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Author:
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Barbieri, Francesco; Espinosa-Anke, Luis; Camacho-Collados, Jose; Schockaert, Steven; Saggion, Horacio
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Abstract:
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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. |
Abstract:
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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. |
Abstract:
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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. |
Rights:
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© ACL, Creative Commons Attribution 4.0 License
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Document type:
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Conference Object Article - Published version |
Published by:
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ACL (Association for Computational Linguistics)
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