The main goal of modeling human conversation is to create agents which can interact with people in both open-ended and goal-oriented scenarios. End-to-end trained neural dialog systems are an important line of research for such generalized dialog models as they do not resort to any situation-specific handcrafting of rules. However, incorporating personalization into such systems is a largely unexplored topic as there are no existing corpora to facilitate such work. In this paper, we present a new dataset of goal-oriented dialogs which are influenced by speaker profiles attached to them. We analyze the shortcomings of an existing end-to-end dialog system based on Memory Networks and propose modifications to the architecture which enable personalization. We also investigate personalization in dialog as a multi-task learning problem, and show that a single model which shares features among various profiles outperforms separate models for each profile.
@article{arxiv.1706.07503,
title = {Personalization in Goal-Oriented Dialog},
author = {Chaitanya K. Joshi and Fei Mi and Boi Faltings},
journal= {arXiv preprint arXiv:1706.07503},
year = {2019}
}
Comments
Accepted at NIPS 2017 Conversational AI Workshop; Code and data at https://github.com/chaitjo/personalized-dialog