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 ...
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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