Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
Table 4: Quantitative evaluation on the Twitter dataset.
['Models', 'Relevance BLEU', 'Relevance ROUGE', 'Relevance Greedy', 'Relevance Average', 'Relevance Extreme', 'Diversity Dist-1', 'Diversity Dist-2', 'Diversity Ent-4']
We further compared our methods on the Twitter dataset. We treated all dialog history before the last response in a multi-turn conversation session as a source sentence, and use the last response as the target to form our dataset. We employed CNN as our encoder because a CNN-based encoder is presumably advantageous in tracking long dialog history comparing to an LSTM encoder. We truncated the vocabulary to contain only 20k most frequent words due to limited flash memory capacity. We evaluated each methods on 2k test data.