Affect-LM: A Neural Language Model for Customizable Affective Text Generation
Table 3: Evaluation perplexity scores obtained by the baseline and Affect-LM models when trained on Fisher and subsequently adapted on DAIC, SEMAINE and CMU-MOSI corpora
['[BOLD] Perplexity', 'Training (Fisher)  [BOLD] Baseline', 'Training (Fisher)  [BOLD] Affect-LM', 'Adaptation  [BOLD] Baseline', 'Adaptation  [BOLD] Affect-LM']
The models trained on Fisher are evaluated on all corpora while each adapted model is evaluated only on it’s respective corpus. For all corpora, we find that Affect-LM achieves lower perplexity on average than the baseline model, implying that affect category information obtained from the context words improves language model prediction. The average perplexity improvement is 1.44 (relative improvement 1.94%) for the model trained on Fisher, while it is 0.79 (1.31%) for the adapted models. We note that larger improvements in perplexity are observed for corpora with higher content of emotional words.