# Sequential Convolutional Neural Networks for Slot Filling in Spoken Language Understanding

arXiv: https://arxiv.org/abs/1606.07783 · HTML: https://mlsanity.com/paper/1606.07783/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot Filling](https://mlsanity.com/paper/2106.07055/) (2021)
- [SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling](https://mlsanity.com/paper/2108.11711/) (2021)
- [Traffic Event Detection as a Slot Filling Problem](https://mlsanity.com/paper/2109.06035/) (2021)
- [ESIE-BERT: Enriching Sub-words Information Explicitly with BERT for Joint Intent Classification and SlotFilling](https://mlsanity.com/paper/2211.14829/) (2022)
- [A survey of joint intent detection and slot-filling models in natural language understanding](https://mlsanity.com/paper/2101.08091/) (2021)

## Abstract (arXiv)

We investigate the usage of convolutional neural networks (CNNs) for the slot filling task in spoken language understanding. We propose a novel CNN architecture for sequence labeling which takes into account the previous context words with preserved order information and pays special attention to the current word with its surrounding context. Moreover, it combines the information from the past and the future words for classification. Our proposed CNN architecture outperforms even the previously best ensembling recurrent neural network model and achieves state-of-the-art results with an F1-score of 95.61% on the ATIS benchmark dataset without using any additional linguistic knowledge and resources.