# JPIS: A Joint Model for Profile-based Intent Detection and Slot Filling with Slot-to-Intent Attention

arXiv: https://arxiv.org/abs/2312.08737 · HTML: https://mlsanity.com/paper/2312.08737/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 28 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 21 of 21)

- [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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [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)
- [Inferring Degrees from Incomplete Networks and Nonlinear Dynamics](https://mlsanity.com/paper/2004.10546/) (2020)
- [GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.01925/) (2021)
- [Intent Detection and Slot Filling for Vietnamese](https://mlsanity.com/paper/2104.02021/) (2021)
- [Joint Multiple Intent Detection and Slot Filling via Self-distillation](https://mlsanity.com/paper/2108.08042/) (2021)
- [A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding](https://mlsanity.com/paper/2211.04023/) (2022)
- [Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding](https://mlsanity.com/paper/2112.11953/) (2021)
- [MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention](https://mlsanity.com/paper/2312.05741/) (2023)
- [Joint Intent Detection And Slot Filling Based on Continual Learning Model](https://mlsanity.com/paper/2102.10905/) (2021)

## Built on by (most cited first, 2 of 2)

- [AFD-SLU: Adaptive Feature Distillation for Spoken Language Understanding](https://mlsanity.com/paper/2509.04821/) (2025)
- [Introducing Visual Scenes and Reasoning: A More Realistic Benchmark for Spoken Language Understanding](https://mlsanity.com/paper/2511.19005/) (2025)

## Abstract (arXiv)

Profile-based intent detection and slot filling are important tasks aimed at reducing the ambiguity in user utterances by leveraging user-specific supporting profile information. However, research in these two tasks has not been extensively explored. To fill this gap, we propose a joint model, namely JPIS, designed to enhance profile-based intent detection and slot filling. JPIS incorporates the supporting profile information into its encoder and introduces a slot-to-intent attention mechanism to transfer slot information representations to intent detection. Experimental results show that our JPIS substantially outperforms previous profile-based models, establishing a new state-of-the-art performance in overall accuracy on the Chinese benchmark dataset ProSLU.