---
title: "Slot-Gated Modeling for Joint Slot Filling and — Goo 2018"
description: "Spoken language understanding (SLU) usually involves two linked subtasks: classifying the intent of an utterance and labeling each word with a semantic…"
authors: ["Chih-Wen Goo", "Guang Gao", "Yun-Kai Hsu", "Chih-Li Huo", "Tsung-Chieh Chen", "Keng-Wei Hsu", "Yun-Nung Chen"]
year: 2018
venue: "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)"
source: "https://aclanthology.org/N18-2118/"
doi: "10.18653/v1/n18-2118"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/goo-2018-slot-gated-modeling-joint-slot/"
---
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# Slot-Gated Modeling for Joint Slot Filling and Intent Prediction

**Authors:** Chih-Wen Goo, Guang Gao, Yun-Kai Hsu, Chih-Li Huo, Tsung-Chieh Chen, Keng-Wei Hsu, [Yun-Nung Chen](https://md.research.elamur.ai/free/en/authors/yun-nung-chen/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N18-2118/) · [PDF](https://aclanthology.org/N18-2118.pdf) · [DOI 10.18653/v1/n18-2118](https://doi.org/10.18653/v1/n18-2118) · [OpenAlex](https://openalex.org/W2803392141)

**Citations:** 497 citations (openalex-snapshot · 2026-10-14) · [156](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Spoken language understanding (SLU) usually involves two linked subtasks: classifying the intent of an utterance and labeling each word with a semantic slot. Earlier joint attention-based RNN models trained both subtasks under a shared loss, but each subtask had its own attention weights. Any interaction between slots and intent was therefore learned only implicitly. Goo et al. add a "slot gate" to a BLSTM attention model. The gate combines the intent context vector with each word's slot context vector and produces a scalar weight that scales the slot context before the slot label is predicted. Slot tagging is thus explicitly conditioned on intent information.

They test two variants, one with full (slot and intent) attention and a lighter one with intent attention only. Both are evaluated on ATIS and Snips against the joint sequence model of Hakkani-Tür et al. and the attention model of Liu and Lane. The gated models improve slot F1, intent accuracy and especially whole-frame accuracy. This shows that explicitly modeling slot–intent dependency benefits joint SLU.

## Key points
- **Datasets:** ATIS has 4,478 / 500 / 893 utterances for train / dev / test, with 120 slot labels and 21 intents. It is single-domain and heavily skewed, with atis_flight making up about 74% of training data. Snips (custom-intent-engines) has 13,084 / 700 / 700 utterances, 72 slots and 7 balanced intents across diverse topics.
- **ATIS results:** The intent-attention-only variant scored best, with 95.2 slot F1, 94.1% intent accuracy and 82.6% sentence accuracy. The attention baseline scored 94.2, 91.1 and 78.9. This variant also uses fewer parameters (251K vs. 284K).
- **Snips results:** The full-attention gated model scored best, with 88.8 F1, 97.0% intent accuracy and 75.5% sentence accuracy, compared with 74.1% sentence accuracy for the attention baseline. The authors suggest that the more complex Snips data still needs separate slot attention.
- **Relative gains:** The paper reports sentence-level gains of roughly 4.2% on ATIS and 1.9% on Snips over the attention model. The paper's own text gives about 4.1% for ATIS in one place. Significance was checked with a one-tailed t-test (p \< 0.05).
- **Setup:** Hidden size 64, the Adam optimizer, early stopping, and scores averaged over 20 runs. Code is available at github.com/MiuLab/SlotGated-SLU.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

156 papers of our library cite it (of the 140673 whose bibliographies we read); 3 of them have a page here, 153 not yet.

- [A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Filling](https://md.research.elamur.ai/free/en/papers/e-2019-novel-bi-directional-interrelated-model/)
- [A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding](https://md.research.elamur.ai/free/en/papers/qin-2019-stack-propagation-framework-token-level/)
- [End-to-End Slot Alignment and Recognition for Cross-Lingual NLU](https://md.research.elamur.ai/free/en/papers/xu-2020-end-end-slot-alignment-recognition/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{goo2018slot,
  title = {{Slot-Gated Modeling for Joint Slot Filling and Intent Prediction}},
  author = {Goo, Chih-Wen and Gao, Guang and Hsu, Yun-Kai and Huo, Chih-Li and Chen, Tsung-Chieh and Hsu, Keng-Wei and Chen, Yun-Nung},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-2118},
  url = {https://doi.org/10.18653/v1/n18-2118}
}
```

- [← previous Simpler but More Accurate Semantic Dependency Parsing](https://md.research.elamur.ai/free/en/papers/dozat-2018-simpler-but-more-accurate-semantic/)
- [next → Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://md.research.elamur.ai/free/en/papers/coucke-2018-snips-voice-platform-embedded-spoken/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/4/)

## References

4 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. [find](https://scholar.google.com/scholar?q=Dzmitry+Bahdanau%2C+Kyunghyun+Cho%2C+and+Yoshua+Bengio%2E+2014%2E+Neural+machine+translation+by+jointly+learning+to+align+and+translate%2E+arXiv+preprint+arXiv%3A1409%2E0473%2E) [our card](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/)
2. Yun-Nung Chen, Dilek Hakanni-Tür, Gokhan Tur, Asli Celikyilmaz, Jianfeng Guo, and Li Deng. 2016. Syntax or semantics? knowledge-guided joint semantic frame parsing. In Proceedings of 2016 IEEE Spoken Language Technology Workshop, pages 348–355. IEEE. [find](https://scholar.google.com/scholar?q=Yun%2DNung+Chen%2C+Dilek+Hakanni%2DTu%CC%88r%2C+Gokhan+Tur%2C+Asli+Celikyilmaz%2C+Jianfeng+Guo%2C+and+Li+Deng%2E+2016%2E+Syntax+or+semantics%3F+knowledge%2Dguided+joint+semantic+frame+parsing%2E+In+Proceedings+of+2016+IEEE+Spoken)
3. Daniel Guo, Gokhan Tur, Wen-tau Yih, and Geoffrey Zweig. 2014. Joint semantic utterance classification and slot filling with recursive neural networks. In Proceedings of 2014 IEEE Spoken Language Technology Workshop, pages 554–559. IEEE. [find](https://scholar.google.com/scholar?q=Daniel+Guo%2C+Gokhan+Tur%2C+Wen%2Dtau+Yih%2C+and+Geoffrey+Zweig%2E+2014%2E+Joint+semantic+utterance+classification+and+slot+filling+with+recursive+neural+networks%2E+In+Proceedings+of+2014+IEEE+Spoken+Language+Tech)
4. Dilek Hakkani-Tür, Gökhan Tür, Asli Celikyilmaz, Yun-Nung Chen, Jianfeng Gao, Li Deng, and Ye-Yi Wang. 2016. Multi-domain joint semantic frame [find](https://scholar.google.com/scholar?q=Dilek+Hakkani%2DTu%CC%88r%2C+Go%CC%88khan+Tu%CC%88r%2C+Asli+Celikyilmaz%2C+Yun%2DNung+Chen%2C+Jianfeng+Gao%2C+Li+Deng%2C+and+Ye%2DYi+Wang%2E+2016%2E+Multi%2Ddomain+joint+semantic+frame)