# CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding

arXiv: https://arxiv.org/abs/1909.06937 · HTML: https://mlsanity.com/paper/1909.06937/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2019.

## Built on by (most cited first, 30 of 34)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods](https://mlsanity.com/paper/2204.03508/) (2022)
- [GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.01925/) (2021)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs](https://mlsanity.com/paper/2210.10375/) (2022)
- [SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling](https://mlsanity.com/paper/2108.11711/) (2021)
- [Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2210.10369/) (2022)
- [Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding](https://mlsanity.com/paper/2306.09114/) (2023)
- [ECLM: Entity Level Language Model for Spoken Language Understanding with Chain of Intent](https://mlsanity.com/paper/2403.04481/) (2024)
- [Advancing Single and Multi-task Text Classification through Large Language Model Fine-tuning](https://mlsanity.com/paper/2412.08587/) (2024)
- [Learning to Bridge Metric Spaces: Few-shot Joint Learning of Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.07343/) (2021)
- [An Effective Non-Autoregressive Model for Spoken Language Understanding](https://mlsanity.com/paper/2108.07005/) (2021)
- [A Context-Aware Hierarchical BERT Fusion Network for Multi-turn Dialog Act Detection](https://mlsanity.com/paper/2109.01267/) (2021)
- [Explainable Slot Type Attentions to Improve Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2210.10227/) (2022)
- [A Result based Portable Framework for Spoken Language Understanding](https://mlsanity.com/paper/2103.06010/) (2021)
- [A Review and Roadmap of Deep Learning Causal Discovery in Different Variable Paradigms](https://mlsanity.com/paper/2209.06367/) (2022)
- [A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding](https://mlsanity.com/paper/2211.12220/) (2022)
- [CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2406.10505/) (2024)
- [Capture Salient Historical Information: A Fast and Accurate Non-Autoregressive Model for Multi-turn Spoken Language Understanding](https://mlsanity.com/paper/2206.12209/) (2022)
- [CTRAN: CNN-Transformer-based Network for Natural Language Understanding](https://mlsanity.com/paper/2303.10606/) (2023)
- [OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding](https://mlsanity.com/paper/2305.10231/) (2023)
- [Co-guiding for Multi-intent Spoken Language Understanding](https://mlsanity.com/paper/2312.03716/) (2023)
- [Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based Spoken Language Understanding](https://mlsanity.com/paper/2402.03900/) (2024)
- [MAC-SLU: Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark](https://mlsanity.com/paper/2512.01603/) (2025)
- [A Joint and Domain-Adaptive Approach to Spoken Language Understanding](https://mlsanity.com/paper/2107.11768/) (2021)
- [Introducing Visual Scenes and Reasoning: A More Realistic Benchmark for Spoken Language Understanding](https://mlsanity.com/paper/2511.19005/) (2025)
- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)
- [HAN: Higher-order Attention Network for Spoken Language Understanding](https://mlsanity.com/paper/2108.11916/) (2021)
- [Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction](https://mlsanity.com/paper/2212.10728/) (2022)

## Abstract (arXiv)

Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works. However, most existing models fail to fully utilize co-occurrence relations between slots and intents, which restricts their potential performance. To address this issue, in this paper we propose a novel Collaborative Memory Network (CM-Net) based on the well-designed block, named CM-block. The CM-block firstly captures slot-specific and intent-specific features from memories in a collaborative manner, and then uses these enriched features to enhance local context representations, based on which the sequential information flow leads to more specific (slot and intent) global utterance representations. Through stacking multiple CM-blocks, our CM-Net is able to alternately perform information exchange among specific memories, local contexts and the global utterance, and thus incrementally enriches each other. We evaluate the CM-Net on two standard benchmarks (ATIS and SNIPS) and a self-collected corpus (CAIS). Experimental results show that the CM-Net achieves the state-of-the-art results on the ATIS and SNIPS in most of criteria, and significantly outperforms the baseline models on the CAIS. Additionally, we make the CAIS dataset publicly available for the research community.