Towards Deep Understanding in Task-Oriented Dialogue Systems Using Deep Reinforcement Learning
Lead Research Organisation:
City, University of London
Department Name: Computing
Abstract
Conversation is a natural way for humans to obtain and absorb information. Several generations of artificial conversational agents have been developed since the 1960's, but we are still a long way from dialog systems which can respond as flexibly and usefully as a human can. Supervised end-to-end Deep Learning (DL) approaches overcome several limitations of previous pipeline methods, but still require large training sets and may suffer from a lack of concept understanding due to an inability to explore beyond the training data. This work intends to deliver improvements in task-oriented dialog systems by incorporating grounded language techniques into Deep Reinforcement Learning (DRL) agents, trained end-to-end in text-based simulations such as the bAbI benchmark [1].
Organisations
People |
ORCID iD |
Eduardo Alonso (Primary Supervisor) | |
Sarah Scott (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
EP/R513015/1 | 01/10/2018 | 30/09/2023 | |||
2292077 | Studentship | EP/R513015/1 | 01/10/2019 | 15/01/2021 | Sarah Scott |