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license: cc0-1.0
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# Minecraft Structured Dialogue Corpus
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The Minecraft Structured Dialogue Corpus (MSDC) is a discourse-annotated version of the
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The MSDC features complete, situated discourse structures for each dialogue in the style of Situated Discourse Representation Theory (Asher and Lascarides, 2003).
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**Dataset Description**
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**Citations**
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Please cite the paper below if using the MSDC in your work.
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**Description of the MSDC :
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Kate Thompson, Julie Hunter and Nicholas Asher, 2024. [Discourse Structure for the Minecraft Corpus](https://aclanthology.org/2024.lrec-main.444/). In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 4957–4967, Torino, Italia. ELRA and ICCL.
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title = "Discourse Structure for the {M}inecraft Corpus",
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author = "Thompson, Kate and
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Hunter, Julie and
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publisher = "ELRA and ICCL",
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url = "https://aclanthology.org/2024.lrec-main.444/",
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pages = "4957--4967"
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}
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**Acknowledgements**
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We acknowledge support from the National Interdisciplinary Artificial Intelligence Institute, ANITI (Artificial and Natural Intelligence Toulouse Institute), funded by the French ‘Investing for the
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Future–PIA3’ program under the Grant agreement ANR-19-PI3A-000. We also thank the ANR project COCOBOTS (ANR-21-FAI2-0005), the ANR/DGA project DISCUTER (ANR21-ASIA-0005), and the COCOPIL “Graine” project funded by the Région Occitanie of France.
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license: cc0-1.0
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language:
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- en
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# Minecraft Structured Dialogue Corpus
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The Minecraft Structured Dialogue Corpus (MSDC) is a discourse-annotated version of the [Minecraft Dialogue Corpus (MDC)](https://juliahmr.cs.illinois.edu/Minecraft/ACL2019.html), first created by Julia Hockenmaier's lab in 2019.
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The MSDC features complete, situated discourse structures for each dialogue in the style of Situated Discourse Representation Theory (Asher and Lascarides, 2003).
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**Dataset Description**
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The MSDC is a collection of natural language dialogues between pairs of human participants who meet in a simulated 3D Minecraft grid and communicate through a chat window. One assumes the role of Architect, and the other the role of Builder. At the beginning of each dialogue, the Architect is shown an image of a structure made from colored blocks, and the goal of the interaction is to help the Builder recreate the structure on the grid via typed Architect instructions.
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The chat interaction between the Architect and Builder produces linguistic speaker turns, while the Builder actions (the placement and removal of blocks) are represented textually, e.g. *place orange 2 1 -1*, and attributed to Builder as speaker.
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A small group of discourse experts and students annotated the corpus. First they hand-segmented the linguistic turns into elementary discourse units (EDUs). Each Builder action turn was considered a single elementary event unit (EEU). All but *7* of the dialogues in the MDC were annotated, and the original MDC splits were preserved, except for a small Development set culled from Test for the purpose of developing the [Llamipa discourse parser](https://huggingface.co/linagora/Llamipa).
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| | Train | Val | Test | Dev | Total |
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|----------------|-------|--------|------|------|------|
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|No. Dialogues | 307| 100 | 101 | 32 | 540|
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|No. EDUs | 9616 | 3297 | 3009 | 1128 | 17050 |
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|No. EEUs |6669 | 2240 | 2006 | 732 | 11647|
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|No. Relation instances |19598 | 6681 | 6018 | 2232 |34529|
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**Citations**
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Please cite the paper below if using the MSDC in your work.
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Kate Thompson, Julie Hunter and Nicholas Asher, 2024. [Discourse Structure for the Minecraft Corpus](https://aclanthology.org/2024.lrec-main.444/). In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 4957–4967, Torino, Italia. ELRA and ICCL.
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<!-- >@inproceedings{thompson-etal-2024-discourse,
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title = "Discourse Structure for the {M}inecraft Corpus",
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author = "Thompson, Kate and
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Hunter, Julie and
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publisher = "ELRA and ICCL",
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url = "https://aclanthology.org/2024.lrec-main.444/",
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pages = "4957--4967"
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} -->
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**Acknowledgements**
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We acknowledge support from the National Interdisciplinary Artificial Intelligence Institute, ANITI (Artificial and Natural Intelligence Toulouse Institute), funded by the French ‘Investing for the
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Future–PIA3’ program under the Grant agreement ANR-19-PI3A-000. We also thank the ANR project COCOBOTS (ANR-21-FAI2-0005), the ANR/DGA project DISCUTER (ANR21-ASIA-0005), and the COCOPIL “Graine” project funded by the Région Occitanie of France.
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