# PIN: A Novel Parallel Interactive Network for Spoken Language Understanding

arXiv: https://arxiv.org/abs/2009.13431 · HTML: https://mlsanity.com/paper/2009.13431/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation](https://mlsanity.com/paper/2111.04318/) (2021)
- [Joint Multiple Intent Detection and Slot Filling via Self-distillation](https://mlsanity.com/paper/2108.08042/) (2021)
- [Towards Joint Intent Detection and Slot Filling via Higher-order Attention](https://mlsanity.com/paper/2109.08890/) (2021)
- [A Dynamic Graph Interactive Framework with Label-Semantic Injection for Spoken Language Understanding](https://mlsanity.com/paper/2211.04023/) (2022)
- [Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model](https://mlsanity.com/paper/2107.01571/) (2021)
- [Calibrate and Refine! A Novel and Agile Framework for ASR-error Robust Intent Detection](https://mlsanity.com/paper/2205.11008/) (2022)
- [Introducing Visual Scenes and Reasoning: A More Realistic Benchmark for Spoken Language Understanding](https://mlsanity.com/paper/2511.19005/) (2025)
- [HAN: Higher-order Attention Network for Spoken Language Understanding](https://mlsanity.com/paper/2108.11916/) (2021)
- [Feature-augmented Machine Reading Comprehension with Auxiliary Tasks](https://mlsanity.com/paper/2211.09438/) (2022)
- [Improve Text Classification Accuracy with Intent Information](https://mlsanity.com/paper/2212.07649/) (2022)
- [I2KD-SLU: An Intra-Inter Knowledge Distillation Framework for Zero-Shot Cross-Lingual Spoken Language Understanding](https://mlsanity.com/paper/2310.02594/) (2023)

## Abstract (arXiv)

Spoken Language Understanding (SLU) is an essential part of the spoken dialogue system, which typically consists of intent detection (ID) and slot filling (SF) tasks. Recently, recurrent neural networks (RNNs) based methods achieved the state-of-the-art for SLU. It is noted that, in the existing RNN-based approaches, ID and SF tasks are often jointly modeled to utilize the correlation information between them. However, we noted that, so far, the efforts to obtain better performance by supporting bidirectional and explicit information exchange between ID and SF are not well studied.In addition, few studies attempt to capture the local context information to enhance the performance of SF. Motivated by these findings, in this paper, Parallel Interactive Network (PIN) is proposed to model the mutual guidance between ID and SF. Specifically, given an utterance, a Gaussian self-attentive encoder is introduced to generate the context-aware feature embedding of the utterance which is able to capture local context information. Taking the feature embedding of the utterance, Slot2Intent module and Intent2Slot module are developed to capture the bidirectional information flow for ID and SF tasks. Finally, a cooperation mechanism is constructed to fuse the information obtained from Slot2Intent and Intent2Slot modules to further reduce the prediction bias.The experiments on two benchmark datasets, i.e., SNIPS and ATIS, demonstrate the effectiveness of our approach, which achieves a competitive result with state-of-the-art models. More encouragingly, by using the feature embedding of the utterance generated by the pre-trained language model BERT, our method achieves the state-of-the-art among all comparison approaches.