Joint Multiple Intent Detection and Slot Labeling for Goal-Oriented Dialog (N19-1)
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| Challenge: | Neural network models have gained traction for sentence-level intent classification and token-based slot-label identification. |
| Approach: | They propose a neural network model that performs multi-label classification for identifying multiple intents and produces token-based slot-l labels at the token-level. |
| Outcome: | The proposed model provides a small but statistically significant improvement on the ATIS dataset and 55% accuracy improvement on an internal multi-intent dataset. |
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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. |
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Dual Class Knowledge Propagation Network for Multi-label Few-shot Intent Detection (2023.acl-long)
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| Challenge: | Existing studies on multi-label intent detection are confused by the identical representation of the utterance with multiple labels and overlook the intrinsic intra-class and inter-class relations. |
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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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| 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. |
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LCAN: A Label-Aware Contrastive Attention Network for Multi-Intent Recognition and Slot Filling in Task-Oriented Dialogue Systems (2025.findings-emnlp)
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| Challenge: | Multi-intent utterances processing remains a persistent challenge due to intricate intent-slot dependencies and semantic ambiguities. |
| Approach: | They propose a label-aware contrastive attention network (LCAN) that integrates label-based attention and contrastive learning strategies to improve semantic understanding and generalization in multi-intent scenarios. |
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MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention (2023.findings-emnlp)
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| Challenge: | Existing models for detecting multiple intents and filling slots are based on graphs but face problems . a joint model can exploit the correlations between intents, slots and slot filling tasks . |
| Approach: | They propose a joint model that captures correlations between intents and slot labels . they propose MISCA to incorporate an intent-slot co-attention mechanism and a label attention mechanism . |
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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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Recent Neural Methods on Slot Filling and Intent Classification for Task-Oriented Dialogue Systems: A Survey (2020.coling-main)
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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. |
| Approach: | They propose three neural architectures to model slot filling and intent classification . they propose independent models, joint models and transfer learning models that exploit the mutual benefit of the two tasks simultaneously and scale the model to new domains. |
| Outcome: | The proposed models model SF and IC separately, exploit mutual benefit of the two tasks simultaneously and scale the model to new domains. |
A Pointer Network-based Approach for Joint Extraction and Detection of Multi-Label Multi-Class Intents (2024.findings-emnlp)
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| Challenge: | Existing research focuses on simple queries with a single intent, lacking effective systems for handling complex queries with multiple intents. |
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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. |
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