Emotional Neural Language Generation Grounded in Situational Contexts
Table 3: Comparison of the performance of our model to the baseline model proposed by Rashkin et al (2019) across a variety of automated metrics to provide a thorough comparison. x indicates that these metrics were not provided in the Rashkin et al (2019) work.
['[BOLD] Experiment', '[BOLD] Valid PPL', '[BOLD] BLEU', '[BOLD] Readability', '[BOLD] Coherence', '[BOLD] Length', '[BOLD] Diversity']
We first compare the performance of our approach with the baseline results obtained from Rashkin et al. that uses a full transformer architecture Vaswani et al. We find that fine-tuning the GPT-2 language model using a transfer learning approach helps us achieve a lower perplexity and a higher BLEU scores. The results from our approach are consistent with the empirical study conducted by Edunov et al that demonstrate the effectiveness of the using pre-trained model diminishes when added to the decoder network in an seq2seq approach. We also perform a comparison between our two models on the metrics of length, diversity, readability and coherence. We find that our baseline model produces less diverse responses compared to when the model is conditioned on emotion. We find that the our emo-prepend model also higher a slightly higher readability score that our baseline model.