Tri-level Joint Natural Language Understanding for Multi-turn
  Conversational Datasets
Henry Weld Sijia Hu Siqu Long Josiah Poon Soyeon Caren Han
Natural language understanding typically maps single utterances to a dual
level semantic frame, sentence level intent and slot labels at the word level.
The best performing models force explicit interaction between intent detection
and slot filling. We present a novel tri-level joint natural language
understanding approach, adding domain, and explicitly exchange semantic
information between all levels. This approach enables the use of multi-turn
datasets which are a more natural conversational environment than single
utterance. We evaluate our model on two multi-turn datasets for which we are
the first to conduct joint slot-filling and intent detection. Our model
outperforms state-of-the-art joint models in slot filling and intent detection
on multi-turn data sets. We provide an analysis of explicit interaction
locations between the layers. We conclude that including domain information
improves model performance.