  Emotion Representation Mapping (ERM) has the goal to convert existing emotion
ratings from one representation format into another one, e.g., mapping
Valence-Arousal-Dominance annotations for words or sentences into Ekman's Basic
Emotions and vice versa. ERM can thus not only be considered as an alternative
to Word Emotion Induction (WEI) techniques for automatic emotion lexicon
construction but may also help mitigate problems that come from the
proliferation of emotion representation formats in recent years. We propose a
new neural network approach to ERM that not only outperforms the previous
state-of-the-art. Equally important, we present a refined evaluation
methodology and gather strong evidence that our model yields results which are
(almost) as reliable as human annotations, even in cross-lingual settings.
Based on these results we generate new emotion ratings for 13 typologically
diverse languages and claim that they have near-gold quality, at least.
