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. |
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| Challenge: | Existing joint models only use training procedure to determine the implicit correlation between intents and slots. |
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| Challenge: | Recent research shows promising results by jointly learning of slot filling and intent detection tasks. |
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| Challenge: | In recent years, neural-network based models have been used for a wide range of tasks, including slot filling and intent classification. |
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| Challenge: | Intent classification and slot filling are key building blocks in task-oriented dialogue systems. |
| Approach: | They propose an explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. |
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Data Query Language and Corpus Tools for Slot-Filling and Intent Classification Data (2020.lrec-1)
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| Challenge: | Typical machine learning approaches require large amounts of training data . Managing training data can be cumbersome without dedicated tools . |
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| Challenge: | Existing slot filling models can only recognize pre-defined in-domain slot types from a limited slot set. |
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