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Emotion Representation Mapping (ERM) has the goal to convert existing emotionratings from one representation format into another one, e.g., mappingValence-Arousal-Dominance annotations for words or sentences into Ekman's BasicEmotions and vice versa. ERM can thus not only be considered as an alternativeto Word Emotion Induction (WEI) techniques for automatic emotion lexiconconstruction but may also help mitigate problems that come from theproliferation of emotion representation formats in recent years. We propose anew neural network approach to ERM that not only outperforms the previousstate-of-the-art. Equally important, we present a refined evaluationmethodology 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 typologicallydiverse languages and claim that they have near-gold quality, at least.

Emotion Representation Mapping for Automatic Lexicon Construction (Mostly) Performs on Human Level