Neural Emoji Recommendation in Dialogue Systems
This work addresses the challenge of enhancing anthropomorphic and vivid communication in dialogue systems for users on social platforms, representing an incremental improvement in emoji recommendation.
The paper tackled the problem of automatically recommending appropriate emojis in multi-turn dialogue systems by proposing a hierarchical LSTM model to capture contextual information, achieving the best performance on all evaluation metrics in experiments on a real-world dataset.
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.