论文标题
有关插槽填充和意图分类的最新神经方法，以任务为导向的对话系统：调查
Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey
论文作者
论文摘要
近年来，通过深度学习技术和对会话AI的高需求培养，已经提出了各种方法，以解决引起和了解用户在任务对话系统中的需求的能力。我们专注于两个核心任务：插槽填充（SF）和意图分类（IC），并调查了基于神经的模型如何迅速发展以解决对话系统中的自然语言理解。我们介绍了三个神经体系结构：独立模型，它们分别建模SF和IC，共同模型，它们同时利用这两个任务的相互益处，并传输学习模型，将模型扩展到新领域。我们讨论了SF和IC研究的当前状态，并突出了仍需要关注的挑战。
In recent years, fostered by deep learning technologies and by the high demand for conversational AI, various approaches have been proposed that address the capacity to elicit and understand user's needs in task-oriented dialogue systems. We focus on two core tasks, slot filling (SF) and intent classification (IC), and survey how neural-based models have rapidly evolved to address natural language understanding in dialogue systems. We introduce three neural architectures: independent model, which model SF and IC separately, joint models, which exploit the mutual benefit of the two tasks simultaneously, and transfer learning models, that scale the model to new domains. We discuss the current state of the research in SF and IC and highlight challenges that still require attention.
