# Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling

arXiv: https://arxiv.org/abs/2210.10369 · HTML: https://mlsanity.com/paper/2210.10369/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 47 references that are in the map.
- First posted on arXiv 19 Oct 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://mlsanity.com/paper/1909.02151/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://mlsanity.com/paper/1909.02188/) (2019)
- [Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme](https://mlsanity.com/paper/1706.05075/) (2017)
- [A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1907.00390/) (2019)
- [Hierarchical Graph Network for Multi-hop Question Answering](https://mlsanity.com/paper/1911.03631/) (2019)
- [Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1812.09471/) (2018)
- [A Co-Interactive Transformer for Joint Slot Filling and Intent Detection](https://mlsanity.com/paper/2010.03880/) (2020)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [SlotRefine: A Fast Non-Autoregressive Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2010.02693/) (2020)
- [CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding](https://mlsanity.com/paper/1909.06937/) (2019)
- [GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.01925/) (2021)
- [Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification](https://mlsanity.com/paper/2012.13260/) (2020)
- [Understand me, if you refer to Aspect Knowledge: Knowledge-aware Gated Recurrent Memory Network](https://mlsanity.com/paper/2108.02352/) (2021)
- [DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition](https://mlsanity.com/paper/2203.03856/) (2022)
- [Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2106.10816/) (2021)
- [DigNet: Digging Clues from Local-Global Interactive Graph for Aspect-level Sentiment Classification](https://mlsanity.com/paper/2201.00989/) (2022)
- [Neural Subgraph Explorer: Reducing Noisy Information via Target-Oriented Syntax Graph Pruning](https://mlsanity.com/paper/2205.10970/) (2022)

## Built on by (most cited first, 10 of 10)

- [Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding](https://mlsanity.com/paper/2306.09114/) (2023)
- [MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention](https://mlsanity.com/paper/2312.05741/) (2023)
- [Co-evolving Graph Reasoning Network for Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2306.04340/) (2023)
- [Co-guiding for Multi-intent Spoken Language Understanding](https://mlsanity.com/paper/2312.03716/) (2023)
- [A BiRGAT Model for Multi-intent Spoken Language Understanding with Hierarchical Semantic Frames](https://mlsanity.com/paper/2402.18258/) (2024)
- [MIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLU](https://mlsanity.com/paper/2408.08144/) (2024)
- [Exploiting Contextual Target Attributes for Target Sentiment Classification](https://mlsanity.com/paper/2312.13766/) (2023)
- [HC2L: Hybrid and Cooperative Contrastive Learning for Cross-lingual Spoken Language Understanding](https://mlsanity.com/paper/2405.06204/) (2024)
- [Towards Spoken Language Understanding via Multi-level Multi-grained Contrastive Learning](https://mlsanity.com/paper/2405.20852/) (2024)
- [Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges](https://mlsanity.com/paper/2512.11258/) (2025)

## Abstract (arXiv)

Recent joint multiple intent detection and slot filling models employ label embeddings to achieve the semantics-label interactions. However, they treat all labels and label embeddings as uncorrelated individuals, ignoring the dependencies among them. Besides, they conduct the decoding for the two tasks independently, without leveraging the correlations between them. Therefore, in this paper, we first construct a Heterogeneous Label Graph (HLG) containing two kinds of topologies: (1) statistical dependencies based on labels' co-occurrence patterns and hierarchies in slot labels; (2) rich relations among the label nodes. Then we propose a novel model termed ReLa-Net. It can capture beneficial correlations among the labels from HLG. The label correlations are leveraged to enhance semantic-label interactions. Moreover, we also propose the label-aware inter-dependent decoding mechanism to further exploit the label correlations for decoding. Experiment results show that our ReLa-Net significantly outperforms previous models. Remarkably, ReLa-Net surpasses the previous best model by over 20% in terms of overall accuracy on MixATIS dataset.