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.

翻译：当前对话式AI系统采用多种机器学习流水线，以及外部知识源和业务逻辑来预测下一步动作。在对话管理器流水线中维护多个组件增加了扩展和更新的复杂性，延长了处理时间，并导致通过流水线产生的附加噪声，从而可能造成下一步动作预测错误。本文研究了将图集成到语言变换器中的方法，以增强对人类话语、先前动作和下一步动作之间关系的理解，同时无需依赖外部源或组件。对真实通话的实验分析表明，所提出的图集成语言变换器模型在实际场景中驱动与人类用户的交互式通话时，能够比其他生产级对话式AI系统获得更高的性能。