# A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding

arXiv: https://arxiv.org/abs/2211.04023 · HTML: https://mlsanity.com/paper/2211.04023/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://mlsanity.com/paper/1909.02188/) (2019)
- [A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1907.00390/) (2019)
- [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)
- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.01925/) (2021)
- [Hierarchically-Refined Label Attention Network for Sequence Labeling](https://mlsanity.com/paper/1908.08676/) (2019)
- [Joint Multiple Intent Detection and Slot Filling via Self-distillation](https://mlsanity.com/paper/2108.08042/) (2021)
- [PIN: A Novel Parallel Interactive Network for Spoken Language Understanding](https://mlsanity.com/paper/2009.13431/) (2020)
- [Towards Joint Intent Detection and Slot Filling via Higher-order Attention](https://mlsanity.com/paper/2109.08890/) (2021)
- [SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling](https://mlsanity.com/paper/2108.11711/) (2021)

## Built on by (most cited first, 9 of 9)

- [Differentially Private Learning with Per-Sample Adaptive Clipping](https://mlsanity.com/paper/2212.00328/) (2022)
- [PoseRAC: Pose Saliency Transformer for Repetitive Action Counting](https://mlsanity.com/paper/2303.08450/) (2023)
- [Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels](https://mlsanity.com/paper/2307.01969/) (2023)
- [JPIS: A Joint Model for Profile-based Intent Detection and Slot Filling with Slot-to-Intent Attention](https://mlsanity.com/paper/2312.08737/) (2023)
- [Feature-augmented Machine Reading Comprehension with Auxiliary Tasks](https://mlsanity.com/paper/2211.09438/) (2022)
- [FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning](https://mlsanity.com/paper/2302.11814/) (2023)
- [TLAG: An Informative Trigger and Label-Aware Knowledge Guided Model for Dialogue-based Relation Extraction](https://mlsanity.com/paper/2303.17119/) (2023)
- [I2KD-SLU: An Intra-Inter Knowledge Distillation Framework for Zero-Shot Cross-Lingual Spoken Language Understanding](https://mlsanity.com/paper/2310.02594/) (2023)
- [Modeling Output-Level Task Relatedness in Multi-Task Learning with Feedback Mechanism](https://mlsanity.com/paper/2404.00885/) (2024)

## Abstract (arXiv)

Multi-intent detection and slot filling joint models are gaining increasing traction since they are closer to complicated real-world scenarios. However, existing approaches (1) focus on identifying implicit correlations between utterances and one-hot encoded labels in both tasks while ignoring explicit label characteristics; (2) directly incorporate multi-intent information for each token, which could lead to incorrect slot prediction due to the introduction of irrelevant intent. In this paper, we propose a framework termed DGIF, which first leverages the semantic information of labels to give the model additional signals and enriched priors. Then, a multi-grain interactive graph is constructed to model correlations between intents and slots. Specifically, we propose a novel approach to construct the interactive graph based on the injection of label semantics, which can automatically update the graph to better alleviate error propagation. Experimental results show that our framework significantly outperforms existing approaches, obtaining a relative improvement of 13.7% over the previous best model on the MixATIS dataset in overall accuracy.