Abstract:Dialogue management plays a vital role in task-oriented dialogue systems, which has become an active area of research in recent years. Despite the promising results brought from deep reinforcement learning, most of the studies need to develop a manual user simulator additionally. To address the time-consuming development of simulator policy, we propose a multi-agent dialogue model where an end-to-end dialogue manager and a user simulator are optimized simultaneously. Different from prior work, we optimize the … Show more
“…Conversational systems or interfaces and question answering: The authors in [21] proposed the best practices for question classification in different languages using convolutional neural networks (CNNs), finding the optimal settings depending on the language and validating their transferability. The authors in [22] addressed the time-consuming development of manual user simulator policy and introduced a multi-agent dialogue model, where an end-to-end dialogue manager and a user simulator are optimized simultaneously for dialogue management by cooperative multi-agent reinforcement learning. Moreover, in [23], the authors proposed a Medical Instructed Real-time Assistant (MIRA) that listens to the user's chief complaint and predicts a specific disease, thus referring the user to a nearby appropriate medical specialist.…”
Nowadays, systems based on artificial intelligence are being developed, leading to impressive achievements in a variety of complex cognitive tasks, matching or even beating humans [...]
“…Conversational systems or interfaces and question answering: The authors in [21] proposed the best practices for question classification in different languages using convolutional neural networks (CNNs), finding the optimal settings depending on the language and validating their transferability. The authors in [22] addressed the time-consuming development of manual user simulator policy and introduced a multi-agent dialogue model, where an end-to-end dialogue manager and a user simulator are optimized simultaneously for dialogue management by cooperative multi-agent reinforcement learning. Moreover, in [23], the authors proposed a Medical Instructed Real-time Assistant (MIRA) that listens to the user's chief complaint and predicts a specific disease, thus referring the user to a nearby appropriate medical specialist.…”
Nowadays, systems based on artificial intelligence are being developed, leading to impressive achievements in a variety of complex cognitive tasks, matching or even beating humans [...]
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