---
title: "Collaborative Dialogue in Minecraft — Narayan-Chen 2019"
description: "As a low-cost alternative to physical robots, the authors define the Minecraft Collaborative Building… 31 references, cited by 65 papers here."
authors: ["Anjali Narayan-Chen", "Prashant Jayannavar", "Julia Hockenmaier"]
year: 2019
venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P19-1537/"
doi: "10.18653/v1/p19-1537"
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/chen-2019-collaborative-dialogue-minecraft/"
---
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# Collaborative Dialogue in Minecraft

**Authors:** Anjali Narayan-Chen, Prashant Jayannavar and [Julia Hockenmaier](https://md.research.elamur.ai/free/en/authors/julia-hockenmaier/) · **Year:** 2019 · **Venue:** [Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2019](https://md.research.elamur.ai/free/en/venues/acl/2019/)) · **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/P19-1537/) · [PDF](https://aclanthology.org/P19-1537.pdf) · [DOI 10.18653/v1/p19-1537](https://doi.org/10.18653/v1/p19-1537) · [OpenAlex](https://openalex.org/W2950801649)

**Citations:** 55 citations (OpenAlex, 2026-10-14) · [65](https://md.research.elamur.ai/free/en/papers/chen-2019-collaborative-dialogue-minecraft/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper targets situated, collaborative dialogue, where agents talk with people about a changing environment to complete a shared task. This is harder than slot-filling systems, chatbots, or conversation about static images. As a low-cost alternative to physical robots, the authors define the Minecraft Collaborative Building Task. An Architect sees a target 3D block structure and, through text chat only, guides a Builder who places and removes colored blocks. The Architect can watch from any viewpoint but cannot act in the world. The authors extended Microsoft's Project Malmo into a logging platform and collected the Minecraft Dialogue Corpus: 509 human-human games with chat, screenshots and full game-state records. As a first benchmark, they study Architect utterance generation. They compare a seq2seq model that reads only the dialogue history with a variant that also encodes global and local world-state features. In human evaluation, world-state information improves correctness, but generated instructions still fall well short of human ones and rarely clarify or correct. The released corpus and platform give a testbed for grounded interactive agents.

## Key points
- **Corpus contents:** the Minecraft Dialogue Corpus has 509 dialogues (15,926 utterances, 113,116 tokens) covering 150 target structures of 6–68 blocks (about 23.5 on average).
- **Splits:** dialogues are split by structure, so test structures are never seen in training. The split is 281 train, 101 dev and 137 test dialogues.
- **Collection setup:** 40 student volunteers took part over about 62 hours. They worked in an 11 × 11 × 9 build region with 120 blocks in six colors. Floating blocks are allowed because Minecraft ignores gravity.
- **Correctness results:** human judges rated 25% of the world-state model's utterances fully correct and 36% partially correct. The dialogue-only seq2seq scored 14% and 28%, and humans reached 89% fully correct.
- **Dialogue-act behavior:** models mostly issue instructions (72–76% of utterances, versus 47% for humans). They almost never correct or clarify (3%, versus 23% for humans). Their outputs often get colors right but show low diversity and some repetition.

*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

- [University of Illinois Urbana-Champaign](https://md.research.elamur.ai/free/en/universities/university-of-illinois-urbana-champaign/) — University, Urbana, United States · 108 works · 67,432 citations

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## Cite

Cite as: Anjali Narayan-Chen, Prashant Jayannavar and Julia Hockenmaier. 2019. Collaborative Dialogue in Minecraft. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

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

```bibtex
@inproceedings{narayanchen2019collaborative,
  title = {{Collaborative Dialogue in Minecraft}},
  author = {Anjali Narayan-Chen, Prashant Jayannavar and Julia Hockenmaier},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1537},
  url = {https://doi.org/10.18653/v1/p19-1537}
}
```

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## References

31 references, 5 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Anne H Anderson, Miles Bader, Ellen Gurman Bard, Elizabeth Boyle, Gwyneth Doherty, Simon Garrod, Stephen Isard, Jacqueline Kowtko, Jan McAllister, Jim Miller, et al. 1991. The HCRC map task corpus. Language and speech, 34(4):351–366. [find](https://scholar.google.com/scholar?q=Anne+H+Anderson%2C+Miles+Bader%2C+Ellen+Gurman+Bard%2C+Elizabeth+Boyle%2C+Gwyneth+Doherty%2C+Simon+Garrod%2C+Stephen+Isard%2C+Jacqueline+Kowtko%2C+Jan+McAllister%2C+Jim+Miller%2C+et+al%2E+1991%2E+The+HCRC+map+task+corpus%2E+La)
2. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. [Neural machine translation by jointly learning to align and translate](https://md.research.elamur.ai/free/en/papers/bahdanau-2014-neural-machine-translation-jointly-learning/). In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings.
3. Yonatan Bisk, Daniel Marcu, and William Wong. 2016a. Towards a dataset for human computer communication via grounded language acquisition. In AAAI Workshop: Symbiotic Cognitive Systems. [find](https://scholar.google.com/scholar?q=Yonatan+Bisk%2C+Daniel+Marcu%2C+and+William+Wong%2E+2016a%2E+Towards+a+dataset+for+human+computer+communication+via+grounded+language+acquisition%2E+In+AAAI+Workshop%3A+Symbiotic+Cognitive+Systems%2E)
4. Yonatan Bisk, Kevin Shih, Yejin Choi, and Daniel Marcu. 2018. Learning interpretable spatial operations in a rich 3D Blocks World. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, pages 5028–5036. [find](https://scholar.google.com/scholar?q=Yonatan+Bisk%2C+Kevin+Shih%2C+Yejin+Choi%2C+and+Daniel+Marcu%2E+2018%2E+Learning+interpretable+spatial+operations+in+a+rich+3D+Blocks+World%2E+In+Proceedings+of+the+Thirty%2DSecond+AAAI+Conference+on+Artificial+Int)
5. Yonatan Bisk, Deniz Yuret, and Daniel Marcu. 2016b. Natural language communication with robots. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 751–761, San Diego, California. Association for Com… [find](https://scholar.google.com/scholar?q=Yonatan+Bisk%2C+Deniz+Yuret%2C+and+Daniel+Marcu%2E+2016b%2E+Natural+language+communication+with+robots%2E+In+Proceedings+of+the+2016+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational)
6. Antoine Bordes, Y.-Lan Boureau, and Jason Weston. 2017. Learning end-to-end goal-oriented dialog. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. [find](https://scholar.google.com/scholar?q=Antoine+Bordes%2C+Y%2E%2DLan+Boureau%2C+and+Jason+Weston%2E+2017%2E+Learning+end%2Dto%2Dend+goal%2Doriented+dialog%2E+In+5th+International+Conference+on+Learning+Representations%2C+ICLR+2017%2C+Toulon%2C+France%2C+April+24%2D26%2C+2)
7. Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gašić. 2018. MultiWOZ - a large-scale multi-domain wizard-of-Oz dataset for task-oriented dialogue modelling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Langua… [find](https://scholar.google.com/scholar?q=Pawe%C5%82+Budzianowski%2C+Tsung%2DHsien+Wen%2C+Bo%2DHsiang+Tseng%2C+In%CC%83igo+Casanueva%2C+Stefan+Ultes%2C+Osman+Ramadan%2C+and+Milica+Gas%CC%8Cic%CC%81%2E+2018%2E+MultiWOZ+%2D+a+large%2Dscale+multi%2Ddomain+wizard%2Dof%2DOz+dataset+for+task%2Dorien)
8. Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, José M.F. Moura, Devi Parikh, and Dhruv Batra. 2017. Visual Dialog. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 326– 335. [find](https://scholar.google.com/scholar?q=Abhishek+Das%2C+Satwik+Kottur%2C+Khushi+Gupta%2C+Avi+Singh%2C+Deshraj+Yadav%2C+Jose%CC%81+M%2EF%2E+Moura%2C+Devi+Parikh%2C+and+Dhruv+Batra%2E+2017%2E+Visual+Dialog%2E+In+Proceedings+of+the+IEEE+Conference+on+Computer+Vision+and+P)
9. Srinivasan Janarthanam, Oliver Lemon, and Xingkun Liu. 2012. A web-based evaluation framework for spatial instruction-giving systems. In Proceedings of the ACL 2012 System Demonstrations, pages 49– 54, Jeju Island, Korea. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Srinivasan+Janarthanam%2C+Oliver+Lemon%2C+and+Xingkun+Liu%2E+2012%2E+A+web%2Dbased+evaluation+framework+for+spatial+instruction%2Dgiving+systems%2E+In+Proceedings+of+the+ACL+2012+System+Demonstrations%2C+pages+49%E2%80%93+54)
10. Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell. 2016. The Malmo platform for artificial intelligence experimentation. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16), pages 4246–4247. [find](https://scholar.google.com/scholar?q=Matthew+Johnson%2C+Katja+Hofmann%2C+Tim+Hutton%2C+and+David+Bignell%2E+2016%2E+The+Malmo+platform+for+artificial+intelligence+experimentation%2E+In+Proceedings+of+the+Twenty%2DFifth+International+Joint+Conference+o)
11. Seokhwan Kim, Luis Fernando D’Haro, Rafael E Banchs, Jason D Williams, and Matthew Henderson. 2017. The fourth dialog state tracking challenge. In Dialogues with Social Robots, pages 435–449. Springer. [find](https://scholar.google.com/scholar?q=Seokhwan+Kim%2C+Luis+Fernando+D%E2%80%99Haro%2C+Rafael+E+Banchs%2C+Jason+D+Williams%2C+and+Matthew+Henderson%2E+2017%2E+The+fourth+dialog+state+tracking+challenge%2E+In+Dialogues+with+Social+Robots%2C+pages+435%E2%80%93449%2E+Springer)
12. Seokhwan Kim, Luis Fernando D’Haro, Rafael E Banchs, Jason D Williams, Matthew Henderson, and Koichiro Yoshino. 2016. The fifth dialog state tracking challenge. In 2016 IEEE Spoken Language Technology Workshop (SLT), pages 511–517. [find](https://scholar.google.com/scholar?q=Seokhwan+Kim%2C+Luis+Fernando+D%E2%80%99Haro%2C+Rafael+E+Banchs%2C+Jason+D+Williams%2C+Matthew+Henderson%2C+and+Koichiro+Yoshino%2E+2016%2E+The+fifth+dialog+state+tracking+challenge%2E+In+2016+IEEE+Spoken+Language+Technology)
13. Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. [find](https://scholar.google.com/scholar?q=Diederik+P%2E+Kingma+and+Jimmy+Ba%2E+2015%2E+Adam%3A+A+method+for+stochastic+optimization%2E+In+3rd+International+Conference+on+Learning+Representations%2C+ICLR+2015%2C+San+Diego%2C+CA%2C+USA%2C+May+7%2D9%2C+2015%2C+Conference)
14. Alexander Koller, Kristina Striegnitz, Donna Byron, Justine Cassell, Robert Dale, Johanna Moore, and Jon Oberlander. 2010. The first challenge on generating instructions in virtual environments. In Empirical Methods in Natural Language Generation, pages 328–352, Berlin, Heidelberg. Springer-Verlag. [find](https://scholar.google.com/scholar?q=Alexander+Koller%2C+Kristina+Striegnitz%2C+Donna+Byron%2C+Justine+Cassell%2C+Robert+Dale%2C+Johanna+Moore%2C+and+Jon+Oberlander%2E+2010%2E+The+first+challenge+on+generating+instructions+in+virtual+environments%2E+In+Em)
15. Jiwei Li, Will Monroe, and Dan Jurafsky. 2016. A simple, fast diverse decoding algorithm for neural generation. arXiv preprint arXiv:1611.08562. [find](https://scholar.google.com/scholar?q=Jiwei+Li%2C+Will+Monroe%2C+and+Dan+Jurafsky%2E+2016%2E+A+simple%2C+fast+diverse+decoding+algorithm+for+neural+generation%2E+arXiv+preprint+arXiv%3A1611%2E08562%2E)
16. Joyce Y. Chai, Qiaozi Gao, Lanbo She, Shaohua Yang, Sari Saba-Sadiya, and Guangyue Xu. 2018. Language to action: Towards interactive task learning with physical agents. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18), pages 2–9. International… [find](https://scholar.google.com/scholar?q=Joyce+Y%2E+Chai%2C+Qiaozi+Gao%2C+Lanbo+She%2C+Shaohua+Yang%2C+Sari+Saba%2DSadiya%2C+and+Guangyue+Xu%2E+2018%2E+Language+to+action%3A+Towards+interactive+task+learning+with+physical+agents%2E+In+Proceedings+of+the+Twenty%2DSe)
17. Ryan Lowe, Nissan Pow, Iulian Serban, and Joelle Pineau. 2015. [The Ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems](https://md.research.elamur.ai/free/en/papers/lowe-2015-ubuntu-dialogue-corpus-large-dataset/). In Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 285–294, Prague, Czech Republi…
18. David Chen and Raymond Mooney. 2011. Learning to interpret natural language navigation instructions from observations. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, pages 859–865. [find](https://scholar.google.com/scholar?q=David+Chen+and+Raymond+Mooney%2E+2011%2E+Learning+to+interpret+natural+language+navigation+instructions+from+observations%2E+In+Proceedings+of+the+Twenty%2DFifth+AAAI+Conference+on+Artificial+Intelligence%2C+pa)
19. Dipendra K. Misra, Jaeyong Sung, Kevin Lee, and Ashutosh Saxena. 2016. Tell me Dave: Context-sensitive grounding of natural language to manipulation instructions. The International Journal of Robotics Research, 35(1-3):281–300. [find](https://scholar.google.com/scholar?q=Dipendra+K%2E+Misra%2C+Jaeyong+Sung%2C+Kevin+Lee%2C+and+Ashutosh+Saxena%2E+2016%2E+Tell+me+Dave%3A+Context%2Dsensitive+grounding+of+natural+language+to+manipulation+instructions%2E+The+International+Journal+of+Robotics)
20. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. [Bleu: a method for automatic evaluation of machine translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In Proceedings of 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational Lin…
21. Sida I. Wang, Samuel Ginn, Percy Liang, and Christopher D. Manning. 2017. Naturalizing a programming language via interactive learning. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 929–938, Vancouver, Canada. Association fo… [find](https://scholar.google.com/scholar?q=Sida+I%2E+Wang%2C+Samuel+Ginn%2C+Percy+Liang%2C+and+Christopher+D%2E+Manning%2E+2017%2E+Naturalizing+a+programming+language+via+interactive+learning%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for)
22. Ramakanth Pasunuru and Mohit Bansal. 2018. Game-based video-context dialogue. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 125–136, Brussels, Belgium. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Ramakanth+Pasunuru+and+Mohit+Bansal%2E+2018%2E+Game%2Dbased+video%2Dcontext+dialogue%2E+In+Proceedings+of+the+2018+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pages+125%E2%80%93136%2C+Brussels%2C+Belgiu)
23. Terry Winograd. 1971. Procedures as a representation for data in a computer program for understanding natural language. Technical report, MIT. Cent. Space Res. [find](https://scholar.google.com/scholar?q=Terry+Winograd%2E+1971%2E+Procedures+as+a+representation+for+data+in+a+computer+program+for+understanding+natural+language%2E+Technical+report%2C+MIT%2E+Cent%2E+Space+Res%2E)
24. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. [GloVe: Global vectors for word representation](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/). In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 1532–1543, Doha, Qatar. Association for Computational Linguistics.
25. Alan Ritter, Colin Cherry, and Bill Dolan. 2010. [Unsupervised modeling of Twitter conversations](https://md.research.elamur.ai/free/en/papers/ritter-2010-unsupervised-modeling-twitter-conversations/). In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 172–180, Los Angeles, California. Association for Computat…
26. Nicolas Schrading, Cecilia Ovesdotter Alm, Ray Ptucha, and Christopher Homan. 2015. An analysis of domestic abuse discourse on Reddit. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2577– 2583, Lisbon, Portugal. Association for Computational Linguist… [find](https://scholar.google.com/scholar?q=Nicolas+Schrading%2C+Cecilia+Ovesdotter+Alm%2C+Ray+Ptucha%2C+and+Christopher+Homan%2E+2015%2E+An+analysis+of+domestic+abuse+discourse+on+Reddit%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+Methods+in+Nat)
27. M. Schuster and K. K. Paliwal. 1997. Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673–2681. [find](https://scholar.google.com/scholar?q=M%2E+Schuster+and+K%2E+K%2E+Paliwal%2E+1997%2E+Bidirectional+recurrent+neural+networks%2E+IEEE+Transactions+on+Signal+Processing%2C+45%2811%29%3A2673%E2%80%932681%2E)
28. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15:1929–1958. [find](https://scholar.google.com/scholar?q=Nitish+Srivastava%2C+Geoffrey+Hinton%2C+Alex+Krizhevsky%2C+Ilya+Sutskever%2C+and+Ruslan+Salakhutdinov%2E+2014%2E+Dropout%3A+A+simple+way+to+prevent+neural+networks+from+overfitting%2E+Journal+of+Machine+Learning+Rese)
29. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104–3112. [find](https://scholar.google.com/scholar?q=Ilya+Sutskever%2C+Oriol+Vinyals%2C+and+Quoc+V+Le%2E+2014%2E+Sequence+to+sequence+learning+with+neural+networks%2E+In+Advances+in+neural+information+processing+systems%2C+pages+3104%E2%80%933112%2E)
30. Stefanie Tellex, Thomas Kollar, Steven Dickerson, Matthew Walter, Ashis Banerjee, Seth Teller, and Nicholas Roy. 2011. Understanding natural language commands for robotic navigation and mobile manipulation. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, pages 1507–151… [find](https://scholar.google.com/scholar?q=Stefanie+Tellex%2C+Thomas+Kollar%2C+Steven+Dickerson%2C+Matthew+Walter%2C+Ashis+Banerjee%2C+Seth+Teller%2C+and+Nicholas+Roy%2E+2011%2E+Understanding+natural+language+commands+for+robotic+navigation+and+mobile+manipul)
31. Jesse Thomason, Shiqi Zhang, Raymond J Mooney, and Peter Stone. 2015. Learning to interpret natural language commands through human-robot dialog. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJ-CAI 2015), pages 1923–1929. [find](https://scholar.google.com/scholar?q=Jesse+Thomason%2C+Shiqi+Zhang%2C+Raymond+J+Mooney%2C+and+Peter+Stone%2E+2015%2E+Learning+to+interpret+natural+language+commands+through+human%2Drobot+dialog%2E+In+Proceedings+of+the+Twenty%2DFourth+International+Join)

*Updated 24 October 2026*