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. |
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| Challenge: | Slot filling and intent detection are two main tasks in spoken language understanding systems. |
| Approach: | They propose a non-autoregressive slot filling model with two-pass iteration mechanism to handle uncoordinated slots problem. |
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An Explicit-Joint and Supervised-Contrastive Learning Framework for Few-Shot Intent Classification and Slot Filling (2021.findings-emnlp)
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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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Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence (2022.emnlp-main)
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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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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. |
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Joint Slot Filling and Intent Detection via Capsule Neural Networks (P19-1)
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| Challenge: | Existing models that label slots and detect intent do not preserve hierarchical relationship between words, slots, and intents. |
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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. |
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GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling (2021.acl-long)
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| Challenge: | Existing joint models for multi-intent SLU only consider intent detection while ignoring slot filling task. |
| Approach: | They propose a non-autoregressive model for joint multiple intent detection and slot filling . their framework is 11.5 times faster than existing joint models . |
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Adversarial Training for Multi-task and Multi-lingual Joint Modeling of Utterance Intent Classification (D18-1)
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| Challenge: | In multi-task and multi-lingual joint modeling, common knowledge can be efficiently utilized among multiple tasks or multiple languages. |
| Approach: | They propose to introduce language-specific adversarial networks and task-specific language adversarials to purge the task or language dependencies of shared networks. |
| Outcome: | The proposed method is demonstrated using Japanese and English data sets for three different utterance intent classification tasks. |
SPM: A Split-Parsing Method for Joint Multi-Intent Detection and Slot Filling (2023.acl-industry)
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| Challenge: | Existing studies focus on utterances with a single intent, but lack the ability to assign slots to each corresponding intent. |
| Approach: | They propose a split-parsing method for joint intent detection and slot filling . they split an input sentence into multiple sub-sentences which contain a single-intent . |
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Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling (2021.findings-acl)
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| Challenge: | Existing few-shot learning methods learn a single task each time with a few examples . but, real-world applications often contain multiple closely related tasks . |
| Approach: | They propose a few-shot joint learning scheme that captures intent and slot relationships from only a handful of examples and adapts the bridged metric space to specific few- shot domain. |
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