# Bi-directional Joint Neural Networks for Intent Classification and Slot Filling

arXiv: https://arxiv.org/abs/2202.13079 · HTML: https://mlsanity.com/paper/2202.13079/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 26 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (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)
- [Joint Slot Filling and Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1812.09471/) (2018)
- [Zero-shot User Intent Detection via Capsule Neural Networks](https://mlsanity.com/paper/1809.00385/) (2018)

## Built on by (most cited first, 8 of 8)

- [ILLUMINER: Instruction-tuned Large Language Models as Few-shot Intent Classifier and Slot Filler](https://mlsanity.com/paper/2403.17536/) (2024)
- [CTRAN: CNN-Transformer-based Network for Natural Language Understanding](https://mlsanity.com/paper/2303.10606/) (2023)
- [New Semantic Task for the French Spoken Language Understanding MEDIA Benchmark](https://mlsanity.com/paper/2403.19727/) (2024)
- [MIDAS: Multi-level Intent, Domain, And Slot Knowledge Distillation for Multi-turn NLU](https://mlsanity.com/paper/2408.08144/) (2024)
- [Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets](https://mlsanity.com/paper/2305.17729/) (2023)
- [Predicting User Intents and Musical Attributes from Music Discovery Conversations](https://mlsanity.com/paper/2411.12254/) (2024)
- [Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction](https://mlsanity.com/paper/2212.10728/) (2022)
- [MC-DRE: Multi-Aspect Cross Integration for Drug Event/Entity Extraction](https://mlsanity.com/paper/2308.06546/) (2023)

## Abstract (arXiv)

Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks proceeded independently. However, more recently joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. In this paper, we propose a bi-directional joint model for intent classification and slot filling, which includes a multi-stage hierarchical process via BERT and bi-directional joint natural language understanding mechanisms, including intent2slot and slot2intent, to obtain mutual performance enhancement between intent classification and slot filling. The evaluations show that our model achieves state-of-the-art results on intent classification accuracy, slot filling F1, and significantly improves sentence-level semantic frame accuracy when applied to publicly available benchmark datasets, ATIS (88.6%) and SNIPS (92.8%).