Teaching Machines to Converse
Table 5.5: RL gains over the mutual information system based on pairwise human judgments.
['Setting', 'RL-win', 'RL-lose', 'Tie']
The proposed RL system does not introduce a significant boost in single-turn response quality (winning 40 percent of time and losing 36 percent of time). This is in line with our expectations, as the RL model is not optimized to predict the next utterance, but rather to increase long-term reward. The RL system produces responses that are significantly easier to answer than does the mutual information system, as demonstrated by the single-turn ease to answer setting (winning 52 percent of time and losing 23 percent of time), and also significantly higher quality multi-turn dialogues, as demonstrated by the multi-turn general quality setting (winning 72 percent of time).