Joint Slot Filling and Intent Detection via Capsule Neural Networks

ACL 2019  ·  Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, Philip S. Yu ·

Being able to recognize words as slots and detect the intent of an utterance has been a keen issue in natural language understanding. The existing works either treat slot filling and intent detection separately in a pipeline manner, or adopt joint models which sequentially label slots while summarizing the utterance-level intent without explicitly preserving the hierarchical relationship among words, slots, and intents. To exploit the semantic hierarchy for effective modeling, we propose a capsule-based neural network model which accomplishes slot filling and intent detection via a dynamic routing-by-agreement schema. A re-routing schema is proposed to further synergize the slot filling performance using the inferred intent representation. Experiments on two real-world datasets show the effectiveness of our model when compared with other alternative model architectures, as well as existing natural language understanding services.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Slot Filling ATIS Capsule-NLU F1 0.952 # 7
Intent Detection ATIS Capsule-NLU Accuracy 95.00 # 14
Intent Detection SNIPS Capsule-NLU Intent Accuracy 97.70 # 6
Slot F1 Score 91.80 # 8


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