  Dialogue policy learning (DPL) is a crucial component of dialogue modelling.
Its primary role is to determine the appropriate abstract response, commonly
referred to as the "dialogue action". Traditional DPL methodologies have
treated this as a sequential decision problem, using pre-defined action
candidates extracted from a corpus. However, these incomplete candidates can
significantly limit the diversity of responses and pose challenges when dealing
with edge cases, which are scenarios that occur only at extreme operating
parameters. To address these limitations, we introduce a novel framework, JoTR.
This framework is unique as it leverages a text-to-text Transformer-based model
to generate flexible dialogue actions. Unlike traditional methods, JoTR
formulates a word-level policy that allows for a more dynamic and adaptable
dialogue action generation, without the need for any action templates. This
setting enhances the diversity of responses and improves the system's ability
to handle edge cases effectively. In addition, JoTR employs reinforcement
learning with a reward-shaping mechanism to efficiently finetune the word-level
dialogue policy, which allows the model to learn from its interactions,
improving its performance over time. We conducted an extensive evaluation of
JoTR to assess its effectiveness. Our extensive evaluation shows that JoTR
achieves state-of-the-art performance on two benchmark dialogue modelling
tasks, as assessed by both user simulators and human evaluators.
