_:n8798c06f26af4e279b9770aef7c41418xb45
<http://schema.org/description>
"Language systems have been of great interest to the research community and\nhave recently reached the mass market through various assistant platforms on\nthe web. Reinforcement Learning methods that optimize dialogue policies have\nseen successes in past years and have recently been extended into methods that\npersonalize the dialogue, e.g. take the personal context of users into account.\nThese works, however, are limited to personalization to a single user with whom\nthey require multiple interactions and do not generalize the usage of context\nacross users. This work introduces a problem where a generalized usage of\ncontext is relevant and proposes two Reinforcement Learning (RL)-based\napproaches to this problem. The first approach uses a single learner and\nextends the traditional POMDP formulation of dialogue state with features that\ndescribe the user context. The second approach segments users by context and\nthen employs a learner per context. We compare these approaches in a benchmark\nof existing non-RL and RL-based methods in three established and one novel\napplication domain of financial product recommendation. We compare the\ninfluence of context and training experiences on performance and find that\nlearning approaches generally outperform a handcrafted gold standard."
<https://deepai.org/profile/mark-hoogendoorn>