Neural Emoji Recommendation in Dialogue Systems
Table 3: Evaluation results of P@1 on different emojis
['Emoji', 'Definition', 'S-LSTM', 'H-LSTM']
For further comparisons, we report the P@1 results on different emoji categories. The evaluation results on different emojis show different performances, which implies that there are indeed existing emojis that are more confusing and harder to be predicted than other emojis. (2) Emojis such as heart and angry are relatively easier to be predicted, since this kind of emojis is more straightforward and is usually used in constrained contexts. (3) On the contrary, predicting emojis such as tears of joy and thinking are more challenging, for these emojis are more ambiguous and complicated. For instance, the emoji tears of joy is usually used to express the compounded feeling mixed with slight sad, helpless and embarrassed. Such compounded emotions could be smoothly replaced by other emojis like cry or nervous according to the contexts. (4) H-LSTM has advantages over S-LSTM almost on every emoji category, especially on those more complicated emojis. It confirms that the information in multi-turn dialogue indeed helps understanding the emotions of more complicated dialogues.