Current Conversational AI systems employ different machine learning
pipelines, as well as external knowledge sources and business logic to predict
the next action. Maintaining various components in dialogue managers' pipeline
adds complexity in expansion and updates, increases processing time, and causes
additive noise through the pipeline that can lead to incorrect next action
prediction. This paper investigates graph integration into language
transformers to improve understanding the relationships between humans'
utterances, previous, and next actions without the dependency on external
sources or components. Experimental analyses on real calls indicate that the
proposed Graph Integrated Language Transformer models can achieve higher
performance compared to other production level conversational AI systems in
driving interactive calls with human users in real-world settings.

本文通过将图形集成到语言转换器中，改善了理解人类话语、先前和下一个动作之间关系的能力，而无需依赖外部来源或组件。实验分析表明，所提出的图形集成语言转换器模型在实际调用中可以达到比其他生产级对话式人工智能系统更高的性能。