Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Table 1: Quantitative evaluation on the Reddit dataset. (∗ is implemented based on [5].)
['Models', 'Relevance BLEU', 'Relevance ROUGE', 'Relevance Greedy', 'Relevance Average', 'Relevance Extreme', 'Diversity Dist-1', 'Diversity Dist-2', 'Diversity Ent-4']
Quantitative evaluation We first evaluated our methods on the Reddit dataset using the relevance and diversity metrics. We truncated the vocabulary to contain only the most frequent 20,000 words. We observe that by incorporating the adversarial loss the diversity of generated responses is improved (cGAN vs. seq2seq). The relevance under most metrics (except for BLEU), increases by a small amount.