Papers with Joint intent detection
Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling (2022.findings-emnlp)
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| Challenge: | Existing methods analyze and compute features collectively for all slot types, and have no way to explain slot filling model decisions. |
| Approach: | They propose a method that learns to generate additional slot type specific features to improve accuracy and provides explanations for slot filling decisions for the first time in a joint NLU model. |
| Outcome: | The proposed model improves on two widely used datasets and provides an explanation for slot filling decisions for the first time. |
Balanced Joint Adversarial Training for Robust Intent Detection and Slot Filling (2020.coling-main)
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| Challenge: | Existing joint models for intent detection and slot filling show insufficient robustness . however, some small changes of inputs can fool the models to produce wrong predictions . |
| Approach: | They propose a joint adversarial training model that generates adversarials to attack the joint model and trains the model to defend against the adversarial examples. |
| Outcome: | The proposed model achieves significantly higher scores and improves robustness on two datasets. |
Towards Unified Spoken Language Understanding Decoding via Label-aware Compact Linguistics Representations (2023.findings-acl)
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| Challenge: | Existing methods for intent detection and slot filling decoders could result in misaligned predictions for both tasks. |
| Approach: | They propose a method that leverages label embeddings to jointly guide the decoding process. |
| Outcome: | The proposed method outperforms existing methods on two single- and multi-intent SLU benchmarks and can be incorporated into existing models. |