A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding

By Wanxiang Che, Yangming Li, Haoyang Wen, Ting-Wei Liu. Conference on Empirical Methods in Natural Language Processing, 2019

295 citations recorded by the source corpus.

Intent detection and slot filling are two main tasks for building a spoken language understanding (SLU) system. The two tasks are closely tied and the slots often highly depend on the intent. In this paper, we propose a novel framework for SLU to better incorporate the intent information, which further guiding the slot filling. In our framework, we adopt a joint model with Stack-Propagation which can directly use the intent information as input for slot filling, thus to capture the intent semantic knowledge. In addition, to further alleviate the error propagation, we perform the token-level intent detection for the Stack-Propagation framework. Experiments on two publicly datasets show that our model achieves the state-of-the-art performance and outperforms other previous methods by a large margin. Finally, we use the Bidirectional Encoder Representation from Transformer (BERT) model in our framework, which further boost our performance in SLU task.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d53733e06efd8fb53e584605ee601af0394d9c3d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ting-Wei Liu
2. Wanxiang Che
3. Qianglong Chen
4. Bernardo Magnini
5. Ji Zhang
6. Xing Gao
7. Samuel Louvan
8. Xiao Xu

In Computer Vision and Pattern Recognition

1. Wanxiang Che
2. Bernardo Magnini
3. Ji Zhang
4. Samuel Louvan
5. Ting-Wei Liu
6. Yin Zhang
7. Yue Zhang

Citations in the corpus

• Continual Few-shot Intent Detection (2022, background): “Popular approaches such as Goo et al. (2018); Qin et al. (2019); Mehri et al. (2020) have achieved promising performance.”
• Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog (2020, methodology): “Follow the prior work (Eric et al., 2017; Madotto et al., 2018; Wen et al., 2018; Wu et al., 2019a; Qin et al., 2019b), we adopt the BLEU and Micro Entity F1 metrics to evaluate model performance.”
• Simple is Better! Lightweight Data Augmentation for Low Resource Slot Filling and Intent Classification (2020, background): “Although neural-based models (Qin et al., 2019; Goo et al., 2018; Mesnil et al., 2015) have achieved stellar performance in slot filling (SF) and intent”