---
title: "Towards Universal Dialogue State Tracking — Ren 2018"
description: "The authors propose StateNet to address all three. 32 references, cited by 55 papers here."
authors: ["Liliang Ren", "Kaige Xie", "Lu Chen", "Kai Yu"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1299/"
doi: "10.18653/v1/d18-1299"
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/ren-2018-universal-dialogue-state-tracking/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ren-2018-universal-dialogue-state-tracking/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2018](https://md.research.elamur.ai/free/en/papers/2018/page/3/) / Towards Universal Dialogue State Tracking

# Towards Universal Dialogue State Tracking

**Authors:** Liliang Ren, [Kaige Xie](https://md.research.elamur.ai/free/en/authors/kaige-xie/), [Lu Chen](https://md.research.elamur.ai/free/en/authors/lu-chen/) and [Kai Yu](https://md.research.elamur.ai/free/en/authors/kai-yu/) · **Year:** 2018 · **Venue:** [Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2018](https://md.research.elamur.ai/free/en/venues/emnlp/2018/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/D18-1299/) · [PDF](https://aclanthology.org/D18-1299.pdf) · [DOI 10.18653/v1/d18-1299](https://doi.org/10.18653/v1/d18-1299) · [OpenAlex](https://openalex.org/W2963641152)

**Citations:** 136 citations (OpenAlex, 2026-10-14) · [55](https://md.research.elamur.ai/free/en/papers/ren-2018-universal-dialogue-state-tracking/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper tackles dialogue state tracking (DST), which means estimating a user's goal as slot-value pairs at each turn of a task-oriented spoken dialogue. Many existing trackers have three scaling problems. Some assume a fixed ontology. Others need a separate set of parameters for each slot. Others rely on hand-built semantic dictionaries for delexicalisation. The authors propose StateNet to address all three. It builds n-gram user utterance features weighted by ASR confidence and passes them through a "multi-scale receptors" layer. It combines these with machine act features and gates the result with a slot embedding, then tracks the dialogue over turns with an LSTM. Instead of classifying over a fixed list of values, it outputs a vector the size of a word embedding. Each candidate value is scored by its 2-norm distance to that vector. As a result, values can be added or changed freely and one model can serve every slot. On DSTC2 and WOZ 2.0, the parameter-shared and pre-initialized variant achieved the best joint goal accuracy reported at the time. This points toward trackers that can grow to larger and changing domains.

## Key points
- Three variants were tested: StateNet with separate per-slot models, StateNet PS with parameters shared across slots, and StateNet PSI, which is shared and initialized from a single-slot pre-trained model. StateNet PS uses about a third of the parameters of the separate-model version.
- Joint goal accuracy for StateNet PSI was 75.5 on DSTC2 and 88.9 on WOZ 2.0. The strongest prior baseline, GLAD, scored 74.5 and 88.1.
- Both benchmarks use the same Cambridge restaurant ontology with three slots: food, pricerange and area. WOZ 2.0 has typed rather than spoken input, so it is free of ASR errors.
- Pre-training on the hardest slot, food, worked best (75.5 / 88.9). Pre-training on pricerange gave 73.6 / 88.2, and area gave 73.5 / 87.8.
- The setup uses fixed Paragram-SL999,300-dimensional embeddings, N_c = 128, 4 receptors, bigrams and 3 ASR hypotheses. It was implemented in MXNet 1.1.0 and needs no manually tagged user utterances.

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

## Institutions

- [Shanghai Jiao Tong University](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/) — University, Shanghai, China · 63 works · 15,047 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

🧭 Topics (1): [🗨️ Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · 401 works

## Cite

Cite as: Liliang Ren, Kaige Xie, Lu Chen and Kai Yu. 2018. Towards Universal Dialogue State Tracking. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.

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

```bibtex
@inproceedings{ren2018universal,
  title = {{Towards Universal Dialogue State Tracking}},
  author = {Liliang Ren, Kaige Xie, Lu Chen and Kai Yu},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1299},
  url = {https://doi.org/10.18653/v1/d18-1299}
}
```

- [← previous OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification](https://md.research.elamur.ai/free/en/papers/vajjala-2018-onestopenglish-corpus-new-corpus-automatic/)
- [next → Analyzing Uncertainty in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/ott-2018-analyzing-uncertainty-neural-machine-translation/)
- [↑ back to papers of 2018](https://md.research.elamur.ai/free/en/papers/2018/page/3/)

## References

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

1. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. [Layer normalization](https://md.research.elamur.ai/free/en/papers/ba-2016-layer-normalization/). arXiv preprint arXiv:1607.06450.
2. Lu Chen, Bowen Tan, Sishan Long, and Kai Yu. 2018. Structured dialogue policy with graph neural networks. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1257–1268. [find](https://scholar.google.com/scholar?q=Lu+Chen%2C+Bowen+Tan%2C+Sishan+Long%2C+and+Kai+Yu%2E+2018%2E+Structured+dialogue+policy+with+graph+neural+networks%2E+In+Proceedings+of+the+27th+International+Conference+on+Computational+Linguistics%2C+pages+1257%E2%80%931)
3. Zhehuai Chen, Yimeng Zhuang, and Kai Yu. 2017. Confidence measures for ctc-based phone synchronous decoding. In IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), pages 4850–4854. [find](https://scholar.google.com/scholar?q=Zhehuai+Chen%2C+Yimeng+Zhuang%2C+and+Kai+Yu%2E+2017%2E+Confidence+measures+for+ctc%2Dbased+phone+synchronous+decoding%2E+In+IEEE+International+Conference+on+Acoustics%2C+Speech+and+Signal+Processing%28ICASSP%29%2C+pages+)
4. Matthew Henderson, Blaise Thomson, and Jason D Williams. 2014a. The second dialog state tracking challenge. In Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 263–272, Philadelphia, PA, U.S.A. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Matthew+Henderson%2C+Blaise+Thomson%2C+and+Jason+D+Williams%2E+2014a%2E+The+second+dialog+state+tracking+challenge%2E+In+Proceedings+of+the+15th+Annual+Meeting+of+the+Special+Interest+Group+on+Discourse+and+Dia)
5. Matthew Henderson, Blaise Thomson, and Steve Young. 2014b. Robust dialog state tracking using delexicalised recurrent neural networks and unsupervised adaptation. In Proceedings of IEEE Spoken Language Technology Workshop (SLT). [find](https://scholar.google.com/scholar?q=Matthew+Henderson%2C+Blaise+Thomson%2C+and+Steve+Young%2E+2014b%2E+Robust+dialog+state+tracking+using+delexicalised+recurrent+neural+networks+and+unsupervised+adaptation%2E+In+Proceedings+of+IEEE+Spoken+Languag)
6. Matthew Henderson, Blaise Thomson, and Steve Young. 2014c. [Word-based dialog state tracking with recurrent neural networks](https://md.research.elamur.ai/free/en/papers/henderson-2014-word-based-dialog-state-tracking/). In Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 292–299, Philadelphia, PA, U.S.A. Association for Computation…
7. Mettew Henderson, Blaise Thomson, and Jason D. Williams. 2014d. The third dialog state tracking challenge. In Proceedings of IEEE Spoken Language Technology Workshop (SLT). [find](https://scholar.google.com/scholar?q=Mettew+Henderson%2C+Blaise+Thomson%2C+and+Jason+D%2E+Williams%2E+2014d%2E+The+third+dialog+state+tracking+challenge%2E+In+Proceedings+of+IEEE+Spoken+Language+Technology+Workshop+%28SLT%29%2E)
8. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735–1780. [find](https://scholar.google.com/scholar?q=Sepp+Hochreiter+and+Ju%CC%88rgen+Schmidhuber%2E+1997%2E+Long+short%2Dterm+memory%2E+Neural+computation%2C+9%288%29%3A1735%E2%80%931780%2E)
9. (Volume 1: Long Papers), volume 1, pages 1777– 1788. [find](https://scholar.google.com/scholar?q=%28Volume+1%3A+Long+Papers%29%2C+volume+1%2C+pages+1777%E2%80%93+1788%2E)
10. Julien Perez and Fei Liu. 2017. Dialog state tracking, a machine reading approach using memory network. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, volume 1, pages 305–314. [find](https://scholar.google.com/scholar?q=Julien+Perez+and+Fei+Liu%2E+2017%2E+Dialog+state+tracking%2C+a+machine+reading+approach+using+memory+network%2E+In+Proceedings+of+the+15th+Conference+of+the+European+Chapter+of+the+Association+for+Computation)
11. Osman Ramadan, Paweł Budzianowski, and Milica Gasic. 2018. [Large-scale multi-domain belief tracking with knowledge sharing](https://md.research.elamur.ai/free/en/papers/ramadan-2018-large-scale-multi-domain-belief/). In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), volume 2, pages 432–437.
12. Abhinav Rastogi, Dilek Hakkani-Tur, and Larry Heck. 2017. Scalable multi-domain dialogue state tracking. arXiv preprint arXiv:1712.10224. — [arXiv:1712.10224](https://arxiv.org/abs/1712.10224) [find](https://scholar.google.com/scholar?q=Abhinav+Rastogi%2C+Dilek+Hakkani%2DTur%2C+and+Larry+Heck%2E+2017%2E+Scalable+multi%2Ddomain+dialogue+state+tracking%2E+arXiv+preprint+arXiv%3A1712%2E10224%2E+%E2%80%94+%5BarXiv%3A1712%2E10224%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F1712%2E10224%29)
13. Kai Sun, Lu Chen, Su Zhu, and Kai Yu. 2014a. A generalized rule based tracker for dialogue state tracking. In Proceedings of IEEE Spoken Language Technology Workshop (SLT). [find](https://scholar.google.com/scholar?q=Kai+Sun%2C+Lu+Chen%2C+Su+Zhu%2C+and+Kai+Yu%2E+2014a%2E+A+generalized+rule+based+tracker+for+dialogue+state+tracking%2E+In+Proceedings+of+IEEE+Spoken+Language+Technology+Workshop+%28SLT%29%2E)
14. Kai Sun, Lu Chen, Su Zhu, and Kai Yu. 2014b. The SJTU system for dialog state tracking challenge 2. In Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 318–326, Philadelphia, PA, U.S.A. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Kai+Sun%2C+Lu+Chen%2C+Su+Zhu%2C+and+Kai+Yu%2E+2014b%2E+The+SJTU+system+for+dialog+state+tracking+challenge+2%2E+In+Proceedings+of+the+15th+Annual+Meeting+of+the+Special+Interest+Group+on+Discourse+and+Dialogue+%28S)
15. Kai Sun, Qizhe Xie, and Kai Yu. 2016a. Recurrent polynomial network for dialogue state tracking. Dialogue & Discourse, 7(3):65–88. [find](https://scholar.google.com/scholar?q=Kai+Sun%2C+Qizhe+Xie%2C+and+Kai+Yu%2E+2016a%2E+Recurrent+polynomial+network+for+dialogue+state+tracking%2E+Dialogue+%26+Discourse%2C+7%283%29%3A65%E2%80%9388%2E)
16. Kai Sun, Su Zhu, Lu Chen, Siqiu Yao, Xueyang Wu, and Kai Yu. 2016b. Hybrid dialogue state tracking for real world human-to-human dialogues. In Proc. InterSpeech, pages 2060–2064, San Francisco, America. [find](https://scholar.google.com/scholar?q=Kai+Sun%2C+Su+Zhu%2C+Lu+Chen%2C+Siqiu+Yao%2C+Xueyang+Wu%2C+and+Kai+Yu%2E+2016b%2E+Hybrid+dialogue+state+tracking+for+real+world+human%2Dto%2Dhuman+dialogues%2E+In+Proc%2E+InterSpeech%2C+pages+2060%E2%80%932064%2C+San+Francisco%2C+Americ)
17. Blaise Thomson and Steve Young. 2010. Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems. Computer Speech & Language, 24(4):562–588. [find](https://scholar.google.com/scholar?q=Blaise+Thomson+and+Steve+Young%2E+2010%2E+Bayesian+update+of+dialogue+state%3A+A+POMDP+framework+for+spoken+dialogue+systems%2E+Computer+Speech+%26+Language%2C+24%284%29%3A562%E2%80%93588%2E)
18. Sungjin Lee. 2013. Structured discriminative model for dialog state tracking. In Proceedings of the SIGDIAL 2013 Conference, pages 442–451, Metz, France. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Sungjin+Lee%2E+2013%2E+Structured+discriminative+model+for+dialog+state+tracking%2E+In+Proceedings+of+the+SIGDIAL+2013+Conference%2C+pages+442%E2%80%93451%2C+Metz%2C+France%2E+Association+for+Computational+Linguistics%2E)
19. Zhuoran Wang and Oliver Lemon. 2013. A simple and generic belief tracking mechanism for the dialog state tracking challenge: On the believability of observed information. In Proceedings of the SIGDIAL 2013 Conference, pages 423–432, Metz, France. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Zhuoran+Wang+and+Oliver+Lemon%2E+2013%2E+A+simple+and+generic+belief+tracking+mechanism+for+the+dialog+state+tracking+challenge%3A+On+the+believability+of+observed+information%2E+In+Proceedings+of+the+SIGDIAL)
20. Sungjin Lee and Maxine Eskenazi. 2013. Recipe for building robust spoken dialog state trackers: Dialog state tracking challenge system description. In Proceedings of the SIGDIAL 2013 Conference, pages 414–422, Metz, France. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Sungjin+Lee+and+Maxine+Eskenazi%2E+2013%2E+Recipe+for+building+robust+spoken+dialog+state+trackers%3A+Dialog+state+tracking+challenge+system+description%2E+In+Proceedings+of+the+SIGDIAL+2013+Conference%2C+pages)
21. Tsung-Hsien Wen, David Vandyke, Nikola Mrkšić, Milica Gasic, Lina M. Rojas Barahona, Pei-Hao Su, Stefan Ultes, and Steve Young. 2017. A network-based end-to-end trainable task-oriented dialogue system. In EACL, pages 438–449, Valencia, Spain. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Tsung%2DHsien+Wen%2C+David+Vandyke%2C+Nikola+Mrks%CC%8Cic%CC%81%2C+Milica+Gasic%2C+Lina+M%2E+Rojas+Barahona%2C+Pei%2DHao+Su%2C+Stefan+Ultes%2C+and+Steve+Young%2E+2017%2E+A+network%2Dbased+end%2Dto%2Dend+trainable+task%2Doriented+dialogue+syst)
22. Nikola Mrkšić, Diarmuid Ó Séaghdha, Tsung-Hsien Wen, Blaise Thomson, and Steve Young. 2017. Neural belief tracker: Data-driven dialogue state tracking. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics John Wieting, Mohit Bansal, Kevin Gimpel, Karen Lives… [find](https://scholar.google.com/scholar?q=Nikola+Mrks%CC%8Cic%CC%81%2C+Diarmuid+O%CC%81+Se%CC%81aghdha%2C+Tsung%2DHsien+Wen%2C+Blaise+Thomson%2C+and+Steve+Young%2E+2017%2E+Neural+belief+tracker%3A+Data%2Ddriven+dialogue+state+tracking%2E+In+Proceedings+of+the+55th+Annual+Meeting+of)
23. Jason Williams, Antoine Raux, Deepak Ramachandran, and Alan Black. 2013. The dialog state tracking challenge. In Proceedings of the SIGDIAL 2013 Conference, pages 404–413, Metz, France. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Jason+Williams%2C+Antoine+Raux%2C+Deepak+Ramachandran%2C+and+Alan+Black%2E+2013%2E+The+dialog+state+tracking+challenge%2E+In+Proceedings+of+the+SIGDIAL+2013+Conference%2C+pages+404%E2%80%93413%2C+Metz%2C+France%2E+Association+fo)
24. Kaige Xie, Cheng Chang, Liliang Ren, Lu Chen, and Kai Yu. 2018. Cost-sensitive active learning for dialogue state tracking. In Proceedings of the 19th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL), pages 209– 213, Melbourne, Australia. Association for Computational… [find](https://scholar.google.com/scholar?q=Kaige+Xie%2C+Cheng+Chang%2C+Liliang+Ren%2C+Lu+Chen%2C+and+Kai+Yu%2E+2018%2E+Cost%2Dsensitive+active+learning+for+dialogue+state+tracking%2E+In+Proceedings+of+the+19th+Annual+Meeting+of+the+Special+Interest+Group+on+D)
25. Qizhe Xie, Kai Sun, Su Zhu, Lu Chen, and Kai Yu. 2015. Recurrent polynomial network for dialogue state tracking with mismatched semantic parsers. In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 295–304, Prague, Czech Republic. Association for… [find](https://scholar.google.com/scholar?q=Qizhe+Xie%2C+Kai+Sun%2C+Su+Zhu%2C+Lu+Chen%2C+and+Kai+Yu%2E+2015%2E+Recurrent+polynomial+network+for+dialogue+state+tracking+with+mismatched+semantic+parsers%2E+In+Proceedings+of+the+16th+Annual+Meeting+of+the+Speci)
26. Puyang Xu and Qi Hu. 2018. [An end-to-end approach for handling unknown slot values in dialogue state tracking](https://md.research.elamur.ai/free/en/papers/xu-2018-end-end-approach-handling-unknown/). In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1.
27. Steve Young, Milica Gašić, Simon Keizer, François Mairesse, Jost Schatzmann, Blaise Thomson, and Kai Yu. 2010. The hidden information state model: A practical framework for POMDP-based spoken dialogue management. Computer Speech & Language, 24(2):150–174. [find](https://scholar.google.com/scholar?q=Steve+Young%2C+Milica+Gas%CC%8Cic%CC%81%2C+Simon+Keizer%2C+Franc%CC%A7ois+Mairesse%2C+Jost+Schatzmann%2C+Blaise+Thomson%2C+and+Kai+Yu%2E+2010%2E+The+hidden+information+state+model%3A+A+practical+framework+for+POMDP%2Dbased+spoken+dialo)
28. Steve Young, Milica Gasic, Blaise Thomson, and Jason D. Williams. 2013. POMDP-based statistical spoken dialog systems: A review. Proceedings of the IEEE, 101(5):1160–1179. [find](https://scholar.google.com/scholar?q=Steve+Young%2C+Milica+Gasic%2C+Blaise+Thomson%2C+and+Jason+D%2E+Williams%2E+2013%2E+POMDP%2Dbased+statistical+spoken+dialog+systems%3A+A+review%2E+Proceedings+of+the+IEEE%2C+101%285%29%3A1160%E2%80%931179%2E)
29. Kai Yu, Lu Chen, Bo Chen, Kai Sun, and Su Zhu. 2014. Cognitive technology in task-oriented dialogue systems: Concepts, advances and future. Chinese Journal of Computers, 37(18):1–17. [find](https://scholar.google.com/scholar?q=Kai+Yu%2C+Lu+Chen%2C+Bo+Chen%2C+Kai+Sun%2C+and+Su+Zhu%2E+2014%2E+Cognitive+technology+in+task%2Doriented+dialogue+systems%3A+Concepts%2C+advances+and+future%2E+Chinese+Journal+of+Computers%2C+37%2818%29%3A1%E2%80%9317%2E)
30. Kai Yu, Lu Chen, Kai Sun, Qizhe Xie, and Su Zhu. 2016. Evolvable dialogue state tracking for statistical dialogue management. Frontiers of Computer Science, 10(2):201–215. [find](https://scholar.google.com/scholar?q=Kai+Yu%2C+Lu+Chen%2C+Kai+Sun%2C+Qizhe+Xie%2C+and+Su+Zhu%2E+2016%2E+Evolvable+dialogue+state+tracking+for+statistical+dialogue+management%2E+Frontiers+of+Computer+Science%2C+10%282%29%3A201%E2%80%93215%2E)
31. Kai Yu, Kai Sun, Lu Chen, and Su Zhu. 2015. Constrained markov bayesian polynomial for efficient dialogue state tracking. IEEE/ACM Transactions on Audio, Speech and Language Processing, 23(12):2177–2188. [find](https://scholar.google.com/scholar?q=Kai+Yu%2C+Kai+Sun%2C+Lu+Chen%2C+and+Su+Zhu%2E+2015%2E+Constrained+markov+bayesian+polynomial+for+efficient+dialogue+state+tracking%2E+IEEE%2FACM+Transactions+on+Audio%2C+Speech+and+Language+Processing%2C+23%2812%29%3A2177%E2%80%9321)
32. Victor Zhong, Caiming Xiong, and Richard Socher. 2018. Global-locally self-attentive encoder for dialogue state tracking. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 1458–1467. [find](https://scholar.google.com/scholar?q=Victor+Zhong%2C+Caiming+Xiong%2C+and+Richard+Socher%2E+2018%2E+Global%2Dlocally+self%2Dattentive+encoder+for+dialogue+state+tracking%2E+In+Proceedings+of+the+56th+Annual+Meeting+of+the+Association+for+Computational)

*Updated 24 October 2026*