# SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling

arXiv: https://arxiv.org/abs/2108.11711 · HTML: https://mlsanity.com/paper/2108.11711/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [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)
- [Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1812.09471/) (2018)
- [A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding](https://mlsanity.com/paper/1909.06937/) (2019)
- [Sequential Convolutional Neural Networks for Slot Filling in Spoken Language Understanding](https://mlsanity.com/paper/1606.07783/) (2016)

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

- [CINS: Comprehensive Instruction for Few-shot Learning in Task-oriented Dialog Systems](https://mlsanity.com/paper/2109.04645/) (2021)
- [A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding](https://mlsanity.com/paper/2211.04023/) (2022)
- [A Multi-Granularity Matching Attention Network for Query Intent Classification in E-commerce Retrieval](https://mlsanity.com/paper/2303.15870/) (2023)
- [Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation](https://mlsanity.com/paper/2308.14654/) (2023)
- [BlendX: Complex Multi-Intent Detection with Blended Patterns](https://mlsanity.com/paper/2403.18277/) (2024)
- [A Unified Framework for Multi-intent Spoken Language Understanding with prompting](https://mlsanity.com/paper/2210.03337/) (2022)
- [Differentiable Retrieval Augmentation via Generative Language Modeling for E-commerce Query Intent Classification](https://mlsanity.com/paper/2308.09308/) (2023)
- [A Two-Stage Prediction-Aware Contrastive Learning Framework for Multi-Intent NLU](https://mlsanity.com/paper/2405.02925/) (2024)
- [Surfacing the Unsaid: CUE-Bench for Affective Stance in Chinese Discourse](https://mlsanity.com/paper/2608.10810/) (2026)
- [Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges](https://mlsanity.com/paper/2512.11258/) (2025)

## Abstract (arXiv)

Utterance-level intent detection and token-level slot filling are two key tasks for natural language understanding (NLU) in task-oriented systems. Most existing approaches assume that only a single intent exists in an utterance. However, there are often multiple intents within an utterance in real-life scenarios. In this paper, we propose a multi-intent NLU framework, called SLIM, to jointly learn multi-intent detection and slot filling based on BERT. To fully exploit the existing annotation data and capture the interactions between slots and intents, SLIM introduces an explicit slot-intent classifier to learn the many-to-one mapping between slots and intents. Empirical results on three public multi-intent datasets demonstrate (1) the superior performance of SLIM compared to the current state-of-the-art for NLU with multiple intents and (2) the benefits obtained from the slot-intent classifier.