Adversarial Conversational Shaping for Intelligent Agents
Piotr Tarasiewicz Sultan Kenjeyev Ilana Sebag Shehab Alshehabi
The recent emergence of deep learning methods has enabled the research
community to achieve state-of-the art results in several domains including
natural language processing. However, the current robocall system remains
unstable and inaccurate: text generator and chat-bots can be tedious and
misunderstand human-like dialogue. In this work, we study the performance of
two models able to enhance an intelligent conversational agent through
adversarial conversational shaping: a generative adversarial network with
policy gradient (GANPG) and a generative adversarial network with reward for
every generation step (REGS) based on the REGS model presented in Li et al.
[18] . This model is able to assign rewards to both partially and fully
generated text sequences. We discuss performance with different training
details : seq2seq [ 36] and transformers [37 ] in a reinforcement learning
framework.