Papers with joint multiple intent detection
Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs (2022.emnlp-main)
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| Challenge: | Existing graph-based models only model the unidirectional guidance from intent to slot, which limits the performance. |
| Approach: | They propose a graph-based model that leverages the correlations between intent and slot to achieve mutual guidances between the two tasks. |
| Outcome: | The proposed model outperforms existing models by 19.3% in overall accuracy. |
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 . |
| Outcome: | The proposed model is 11.5 times faster than existing models and is faster than current models. |
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 . |
| Outcome: | The proposed method improves on three multi-intent datasets on multi-tasks. |
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling (2020.findings-emnlp)
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| Challenge: | Existing models focus on the single intent scenario, ignoring the fine-grained multiple intents information integration for token-level slot prediction. |
| Approach: | They propose an Adaptive Graph-Interactive Framework for joint multiple intent detection and slot filling . they propose an intent-slot graph interaction layer to model the strong correlation between the slot and intents . |
| Outcome: | The proposed framework improves on three multi-intent datasets and new state-of-the-art performance on single-intention datasets. |
Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling (2022.emnlp-main)
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| Challenge: | Recent joint multiple intent detection and slot filling models ignore the dependencies among labels and label embeddings. |
| Approach: | They propose to construct a Heterogeneous Label Graph (HLG) containing two kinds of topologies and a novel model termed ReLa-Net which captures beneficial correlations among the labels from HLG. |
| Outcome: | The proposed model outperforms the previous model by over 20% on MixATIS dataset. |
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. |
| Approach: | They propose to make full use of the statistical co-occurrence frequency between intents and slots as prior knowledge to enhance joint multiple intent detection and slot filling. |
| Outcome: | The proposed model outperforms state-of-the-art models on two public multi-intent datasets. |
MRRL: Modifying the Reference via Reinforcement Learning for Non-Autoregressive Joint Multiple Intent Detection and Slot Filling (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive models for multiple intent detection and slot filling have limited overall accuracy due to multi-modality problem and lack of alignment between correct predictions. |
| Approach: | They propose a method for multiple intent detection and slot filling that introduces a modifier and employs reinforcement learning to modify the reference. |
| Outcome: | The proposed method outperforms the previous best approach by 3.6 overall accuracy on MixATIS dataset. |