面向智能体的对抗式对话塑造
Adversarial Conversational Shaping for Intelligent Agents
AI总结：
针对当前对话系统不稳定、准确性不足的问题，研究了两种基于对抗式对话塑造的智能对话增强模型，并在强化学习框架下结合不同训练细节探讨其性能。
摘要：
深度学习方法的兴起已让研究界在自然语言处理等多个领域取得了当前最优结果。但当前的骚扰电话系统仍不稳定且准确性不足：文本生成器与聊天机器人可能表现乏味，还会误解类人对话。本研究探讨了两种可通过对抗式对话塑造增强智能对话体的模型的性能：带策略梯度的生成对抗网络（GANPG），以及基于Li等人[18]提出的REGS模型的、每生成一步均给予奖励的生成对抗网络（REGS），该模型可为部分生成和完全生成的文本序列分配奖励。我们在强化学习框架下，结合seq2seq[36]与transformers[37]等不同训练细节讨论了模型性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
