# Joint Intent Detection And Slot Filling Based on Continual Learning Model

arXiv: https://arxiv.org/abs/2102.10905 · HTML: https://mlsanity.com/paper/2102.10905/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 22 Feb 2021.

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

- [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)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [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 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 Bilingual Generative Transformer for Semantic Sentence Embedding](https://mlsanity.com/paper/1911.03895/) (2019)
- [Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understanding](https://mlsanity.com/paper/1608.02097/) (2016)

## Built on by (most cited first, 4 of 4)

- [JPIS: A Joint Model for Profile-based Intent Detection and Slot Filling with Slot-to-Intent Attention](https://mlsanity.com/paper/2312.08737/) (2023)
- [Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets](https://mlsanity.com/paper/2305.17729/) (2023)
- [Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/2209.04156/) (2022)
- [Multi-Intent Spoken Language Understanding: Methods, Trends, and Challenges](https://mlsanity.com/paper/2512.11258/) (2025)

## Abstract (arXiv)

Slot filling and intent detection have become a significant theme in the field of natural language understanding. Even though slot filling is intensively associated with intent detection, the characteristics of the information required for both tasks are different while most of those approaches may not fully aware of this problem. In addition, balancing the accuracy of two tasks effectively is an inevitable problem for the joint learning model. In this paper, a Continual Learning Interrelated Model (CLIM) is proposed to consider semantic information with different characteristics and balance the accuracy between intent detection and slot filling effectively. The experimental results show that CLIM achieves state-of-the-art performace on slot filling and intent detection on ATIS and Snips.