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首页> 外文会议>3rd workshop on representation learning for NLP 2018 >Chat Discrimination for Intelligent Conversational Agents with a Hybrid CNN-LMTGRU Network
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Chat Discrimination for Intelligent Conversational Agents with a Hybrid CNN-LMTGRU Network

机译：带有CNN-LMTGRU混合网络的智能对话代理的聊天区分

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摘要

Recently, intelligent dialog systems and smart assistants have attracted the attention of many, and development of novel dialogue agents have become a research challenge. Intelligent agents that can handle both domain-specific task-oriented and open-domain chit-chat dialogs are one of the major requirements in the current systems. In order to address this issue and to realize such smart hybrid dialogue systems, we develop a model to discriminate user utterance between task-oriented and chit-chat conversations. We introduce a hybrid of convolutional neural network (CNN) and a lateral multiple timescale gated recurrent units (LMTGRU) that can represent multiple temporal scale dependencies for the discrimination task. With the help of the combined slow and fast units of the LMTGRU, our model effectively determines whether a user will have a chit-chat conversation or a task-specific conversation with the system. We also show that the LMTGRU structure helps the model to perform well on longer text inputs. We address the lack of dataset by constructing a dataset using Twitter and Maluuba Frames data. The results of the experiments demonstrate that the proposed hybrid network outperforms the conventional models on the chat discrimination task as well as performed comparable to the baselines on various benchmark datasets.
机译：近年来，智能对话系统和智能助手已经吸引了许多关注，并且新型对话代理的开发已经成为研究挑战。能够处理特定于领域的面向任务和开放域的聊天对话框的智能代理是当前系统的主要要求之一。为了解决这个问题并实现这种智能的混合对话系统，我们开发了一个模型来区分面向任务的对话和聊天中的用户话语。我们介绍了卷积神经网络（CNN）和横向多个时标门控循环单元（LMTGRU）的混合体，可以表示区分任务的多个时标依赖。借助LMTGRU的慢速和快速单元的组合，我们的模型可以有效地确定用户是否与系统进行聊天对话或特定于任务的对话。我们还表明，LMTGRU结构有助于模型在较长的文本输入上表现良好。我们通过使用Twitter和Maluuba Frames数据构建数据集来解决数据集的不足。实验结果表明，所提出的混合网络在聊天判别任务方面优于常规模型，并且在各种基准数据集上的性能可与基线相比。

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