# Joint model for intent and entity recognition

arXiv: https://arxiv.org/abs/2109.03221 · HTML: https://mlsanity.com/paper/2109.03221/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 13 references that are in the map.
- First posted on arXiv 7 Sep 2021.

## Builds on (most cited first, 9 of 9)

- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (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 Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling](https://mlsanity.com/paper/1812.10235/) (2018)
- [Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling](https://mlsanity.com/paper/1601.01530/) (2016)
- [Recurrent Neural Networks with External Memory for Language Understanding](https://mlsanity.com/paper/1506.00195/) (2015)
- [Jointly Trained Sequential Labeling and Classification by Sparse Attention Neural Networks](https://mlsanity.com/paper/1709.10191/) (2017)

## Built on by (most cited first, 1 of 1)

- [One System to Rule them All: a Universal Intent Recognition System for Customer Service Chatbots](https://mlsanity.com/paper/2112.08261/) (2021)

## Abstract (arXiv)

The semantic understanding of natural dialogues composes of several parts. Some of them, like intent classification and entity detection, have a crucial role in deciding the next steps in handling user input. Handling each task as an individual problem can be wasting of training resources, and also each problem can benefit from each other. This paper tackles these problems as one. Our new model, which combine intent and entity recognition into one system, is achieving better metrics in both tasks with lower training requirements than solving each task separately. We also optimize the model based on the inputs.