Personalization in Goal-oriented Dialog
Table 5: Personalization task 1 (Issuing API calls). The model solves the task successfully by directing its attention towards the memories containing the values of the fields of the API call. The signal grows stronger on the second hop and switches almost exclusively to the price range in the third. This indicates that the model does not need multiple hops for successfully completing PT1. (Zero attention weight signifies a very small value.)
['[BOLD] Time', '[BOLD] Locutor', '[BOLD] Dialog History', '[BOLD] Hop #1', '[BOLD] Hop #2', '[BOLD] Hop #3']
They completed PT1 and PT2 (issuing and updating API calls) with a very high degree of accuracy. This indicates that the model is able to implicitly track dialog state and personalize the bot’s utterance based on the user’s profile. All models were trained on the full dataset.