Graph Integrated Language Transformers for Next Action Prediction in
  Complex Phone Calls
Amin Hosseiny Marani Ulie Schnaithmann Youngseo Son Akil Iyer Manas Paldhe Arushi Raghuvanshi
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.