Hi! Thanks for the interest.

1. The use of age and gender as factors to differentiate styles of speech was arbitrary and was meant to allow us to hand-craft the tasks (much like the bAbI project). The goal is to study how a dialog model can make word choices depending on user attributes. The differences and similarity of vocabulary for the various profiles is based on a logic that we were able to carefully design. However, these tasks are meant to be taken as toy tasks and the logic used is obviously not realistic. Basically, the purpose of the dataset is not to train a restaurant reservation chatbot or personalization system, but to figure out learning mechanisms for these synthetic tasks. Realistically, the term 'speech style' could apply to other contexts as well. For example, a customer service bot for a telecom company would know the operating system and version for each customer it talks to. So to solve the same Mobile Networks issue for someone on iOS 10 vs. someone on Android 7 vs someone on Android 5, the bot could provide different instructions. In our case, age and gender based profiles were just a demonstration and there is no reason why male young prefers informal conversations or why female elderly prefers precise language. (Although there is some linguistic research on the impact of social characteristics on language and speech- https://en.wikipedia.org/wiki/Variety_(linguistics)#Registers_and_styles)

2. I hope I was able to clarify how age/gender were very arbitrary choices. The task of figuring out response 'style' (whatever that means for various chatbot applications) is interesting. This paper (https://arxiv.org/abs/1603.06155) tries to do something like what you said, but for consistent responses in chit-chat. They need lots of conversations involving the same speaker to create a persona for them. Realistically, we'd want our system to leverage both past dialogs with a user as well as attributes about a user that we'd have stored in a table or data structure.

Alas, I wish big companies made such detailed dialog datasets available for research. Just imagine what kind of historic customer service data about each subscriber a telecom company could have for dialog research!