Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets

• URL: http://arxiv.org/abs/2305.17729v1
• Date: Sun, 28 May 2023 13:59:58 GMT
• Title: Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets
• Authors: Henry Weld, Sijia Hu, Siqu Long, Josiah Poon, Soyeon Caren Han
• Abstract summary: We present a novel tri-level joint natural language understanding approach, adding domain, and explicitly exchange semantic information between all levels. We evaluate our model on two multi-turn datasets for which we are the first to conduct joint slot-filling and intent detection.
• Score: 5.3361357265365035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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