# Injecting Word Information with Multi-Level Word Adapter for Chinese Spoken Language Understanding

arXiv: https://arxiv.org/abs/2010.03903 · HTML: https://mlsanity.com/paper/2010.03903/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 7 of 7)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [A Survey on Spoken Language Understanding: Recent Advances and New Frontiers](https://mlsanity.com/paper/2103.03095/) (2021)
- [GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.01925/) (2021)
- [Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding](https://mlsanity.com/paper/2112.11953/) (2021)
- [CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2406.10505/) (2024)
- [Pro-HAN: A Heterogeneous Graph Attention Network for Profile-Based Spoken Language Understanding](https://mlsanity.com/paper/2402.03900/) (2024)
- [HAN: Higher-order Attention Network for Spoken Language Understanding](https://mlsanity.com/paper/2108.11916/) (2021)

## Abstract (arXiv)

In this paper, we improve Chinese spoken language understanding (SLU) by injecting word information. Previous studies on Chinese SLU do not consider the word information, failing to detect word boundaries that are beneficial for intent detection and slot filling. To address this issue, we propose a multi-level word adapter to inject word information for Chinese SLU, which consists of (1) sentence-level word adapter, which directly fuses the sentence representations of the word information and character information to perform intent detection and (2) character-level word adapter, which is applied at each character for selectively controlling weights on word information as well as character information. Experimental results on two Chinese SLU datasets show that our model can capture useful word information and achieve state-of-the-art performance.