Personalizing Dialogue Agents: I have a dog, do you have pets too?
Table 14: Profile Prediction By Dialog Length. Error rates are given for predicting either the persona of speaker 0 (Profile 0) or of speaker 1 (Profile 1) given the dialogue utterances of speaker 0 (PERSON 0) or speaker 1 (PERSON 1). This is shown for dialogues between humans (PERSON 0) and the KV Profile Memory model averaged over the first N dialogue utterances from 100 conversations (where N is the “Dialogue Length”). The results show the accuracy of predicting the persona improves in all cases as dialogue length increases.
['[BOLD] Speaker', '[BOLD] Profile', '[BOLD] Dialogue Length 1', '[BOLD] Dialogue Length 2', '[BOLD] Dialogue Length 3', '[BOLD] Dialogue Length 4', '[BOLD] Dialogue Length 5', '[BOLD] Dialogue Length 6', '[BOLD] Dialogue Length 7', '[BOLD] Dialogue Length 8']
Two tasks could naturally be considered using PersonaChat: (1) next utterance prediction during dialogue, and (2) profile prediction given dialogue history. The main study of this work has been Task 1, where we have shown the use of profile information. Task 2, however, can be used to extract such information. They show ( Combining Task 1 and Task 2 into a full system is an exciting area of future research. We also study the performance of profile prediction as the dialogue progresses, by computing error rates for dialogue lengths 1 to 8 (the longest length we consider in this work).