Adversarial Conversational Shaping for Intelligent Agents

• URL: http://arxiv.org/abs/2307.11785v1
• Date: Thu, 20 Jul 2023 12:44:47 GMT
• Title: Adversarial Conversational Shaping for Intelligent Agents
• Authors: Piotr Tarasiewicz, Sultan Kenjeyev, Ilana Sebag, Shehab Alshehabi
• Abstract summary: We study the performance of two models able to enhance an intelligent conversational agent through adversarial conversational shaping. One 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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