Papers with Snips datasets
Spoken Language Understanding for Task-oriented Dialogue Systems with Augmented Memory Networks (2021.naacl-main)
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| Challenge: | Recent research shows promising results by jointly learning of slot filling and intent detection tasks. |
| Approach: | They propose a way to combine slot filling and slot filler learning to achieve state-of-the-art results. |
| Outcome: | The proposed model outperforms existing methods on benchmark datasets and ATIS datasets. |
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction (N18-2)
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| Challenge: | Existing approaches for slot filling and intent detection have independent attention weights, but they suffer from error propagation due to their independent models. |
| Approach: | They propose a slot gate that focuses on learning the relationship between intent and slot attention vectors to obtain better semantic frame results by the global optimization. |
| Outcome: | The proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively. |
A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling (P19-1)
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| Challenge: | Existing models for slot filling and intent detection lack bi-directional interrelated connections between the intent and slots. |
| Approach: | They propose a bi-directional interrelated model for slot filling and intent detection that uses an SF-ID network to establish direct connections between the two tasks to promote each other mutually. |
| Outcome: | The proposed model improves on ATIS and Snips datasets in sentence-level semantic frame accuracy and improves performance on the two tasks. |