I love your chain mail! Making knights smile in a fantasy game world: Open-domain goal-oriented dialogue agents
Table 9: Top utterances for each verb for the inverse model.
['[BOLD] Verb', '[BOLD] count', '[BOLD] Top utterances']
S6SS0SSS0Px2 Analysis of utterance choice To understand the semantics the models are learning that ground language to actions, we visualize the top scoring utterances, averaged over their probabilities on the 1-step test set, broken down by verb type. We observe a clear improvement in semantic connection for the Topic RL model over the inverse model. For example utterances such as “Have a taste of this” are highly scoring for drink goals, “hmmnnnn.. this sure smells nice” for eat goals, “Ew you vile beast, do not touch me! I will have you removed” for hit goals, and “How I love being pampered by you, sweetheart” for hug goals. Given there are ∼111,000 possible utterances in our setting, the model has clearly learned meaningful representations.