  Emoji is an essential component in dialogues which has been broadly utilized
on almost all social platforms. It could express more delicate feelings beyond
plain texts and thus smooth the communications between users, making dialogue
systems more anthropomorphic and vivid. In this paper, we focus on
automatically recommending appropriate emojis given the contextual information
in multi-turn dialogue systems, where the challenges locate in understanding
the whole conversations. More specifically, we propose the hierarchical long
short-term memory model (H-LSTM) to construct dialogue representations,
followed by a softmax classifier for emoji classification. We evaluate our
models on the task of emoji classification in a real-world dataset, with some
further explorations on parameter sensitivity and case study. Experimental
results demonstrate that our method achieves the best performances on all
evaluation metrics. It indicates that our method could well capture the
contextual information and emotion flow in dialogues, which is significant for
emoji recommendation.
