Neural Emoji Recommendation in Dialogue Systems

TL;DR

This paper introduces a hierarchical LSTM model for recommending emojis in multi-turn dialogues, effectively capturing context and emotion flow to improve emoji prediction accuracy.

Contribution

It presents a novel H-LSTM model tailored for emoji recommendation in dialogue systems, demonstrating superior performance over existing methods.

Findings

Achieves the best performance on emoji classification metrics.

Effectively captures contextual information and emotion flow.

Shows robustness through parameter sensitivity analysis.

Abstract

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.