Joint Emotion Label Space Modelling for Affect Lexica
Table 8: Results aggregated over datasets for GloVe/BERT embeddings and combinations of lexica in Bi-LSTM. See Table 6 for datasets and abbreviations.
['[EMPTY]', 'Single-Label', 'Multi-Label', 'Regression']
We find that, when using GloVe embeddings, adding lexicon information always boosts performance. In most cases (especially for the single-label and regression datasets), adding the naive lexicon concatenation works best, but in some adding the VAE latent emotion space performs better. Overall, the models with GloVe (strongly) outperform the models with only lexicon features, although models with only GloVe embeddings (without lexicon information) never perform better than when lexicon information is added.