TESS: A Multi-intent Parser for Conversational Multi-Agent Systems with
  Decentralized Natural Language Understanding Models
Burak Aksar Yara Rizk Tathagata Chakraborti
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.