Chatbots have become one of the main pathways for the delivery of business automation tools. Multi-agent systems offer a framework for designing chatbots at scale, making it easier to support complex conversations that span across multiple domains as well as enabling developers to maintain and expand their capabilities incrementally over time. However, multi-agent systems complicate the natural language understanding (NLU) of user intents, especially when they rely on decentralized NLU models: some utterances (termed single intent) may invoke a single agent while others (termed multi-intent) may explicitly invoke multiple agents. Without correctly parsing multi-intent inputs, decentralized NLU approaches will not achieve high prediction accuracy. In this paper, we propose an efficient parsing and orchestration pipeline algorithm to service multi-intent utterances from the user in the context of a multi-agent system. Our proposed approach achieved comparable performance to competitive deep learning models on three different datasets while being up to 48 times faster.

翻译：摘要：聊天机器人已成为业务自动化工具交付的主要途径之一。多智能体系统为大规模设计聊天机器人提供了框架，既简化了跨多个领域的复杂对话支持，也使开发人员能够随时间逐步维护和扩展其功能。然而，多智能体系统使用户意图的自然语言理解（NLU）复杂化，尤其在依赖去中心化NLU模型时：部分话语（称为单意图）可能仅调用单个智能体，而其他话语（称为多意图）可能显式调用多个智能体。若无法正确解析多意图输入，去中心化NLU方法将难以实现高预测精度。本文针对多智能体系统中的用户多意图话语，提出了一种高效的解析与编排流程算法。在三个不同数据集上的实验表明，本方法在达到与竞争性深度学习模型相当性能的同时，处理速度提升最高达48倍。