Joint Emotion Label Space Modelling for Affect Lexica
Table 7: Results aggregated over datasets for combinations of lexica in Bi-LSTM. See Table 6 for datasets and abbreviations.
['[EMPTY]', 'Single-Label', 'Multi-Label', 'Regression']
We compare the same three scenarios as in the previous section (naive concatenation without VAE latent emotion space, the VAE latent emotion space on its own, and a naive concatenation with the VAE dimensions included). For brevity, we only report the results where the weights of the sentence vector were updated while training. We also test using the lexicon scores as input as such (fixed), but overall, this performs worse. This might be due to the domain discrepancy between datasets and lexica (even though we combined different lexica). Therefore, we hypothesise that training the VAE jointly with the classification network would perform better. This is something future research will need to confirm.