Interactive Grounded Language Understanding in a Collaborative Environment: Retrospective on Iglu 2022 Competition

Julia Kiseleva, Alexey Skrynnik, Artem Zholus, Shrestha Mohanty, Negar Arabzadeh, Marc-Alexandre Côté, Mohammad Aliannejadi, Milagro Teruel, Ziming Li, Mikhail Burtsev, Maartje ter Hoeve, Zoya Volovikova, Aleksandr Panov, Yuxuan Sun, Kavya Srinet, Arthur Szlam, Ahmed Awadallah, Seungeun Rho, Taehwan Kwon, Daniel Wontae Nam, Felipe Bivort Haiek, Edwin Zhang, Linar Abdrazakov, Guo Qingyam, Jason Zhang, Zhibin Guo
Proceedings of the NeurIPS 2022 Competitions Track, PMLR 220:204-216, 2022.

Abstract

Human intelligence possesses the extraordinary ability to adapt rapidly to new tasks and multi-modal environments. This capacity emerges at an early age, as humans acquire new skills and learn to solve problems by imitating others or following natural language instructions. To facilitate research in this area, we recently hosted the second \emph{IGLU: Interactive Grounded Language Understanding in a Collaborative Environment} competition. The primary objective of the competition is to address the challenge of creating interactive agents that can learn to solve complex tasks by receiving grounded natural language instructions in a collaborative environment. Given the complexity of this challenge, we divided it into two sub-tasks: first, deciding whether the provided grounded instruction requires clarification, and second, following a clear grounded instruction to complete the task description.

Cite this Paper

BibTeX
@InProceedings{pmlr-v220-kiseleva23a, title = {Interactive Grounded Language Understanding in a Collaborative Environment: Retrospective on Iglu 2022 Competition}, author = {Kiseleva, Julia and Skrynnik, Alexey and Zholus, Artem and Mohanty, Shrestha and Arabzadeh, Negar and C\^{o}t\'e, Marc-Alexandre and Aliannejadi, Mohammad and Teruel, Milagro and Li, Ziming and Burtsev, Mikhail and ter Hoeve, Maartje and Volovikova, Zoya and Panov, Aleksandr and Sun, Yuxuan and Srinet, Kavya and Szlam, Arthur and Awadallah, Ahmed and Rho, Seungeun and Kwon, Taehwan and Wontae Nam, Daniel and Bivort Haiek, Felipe and Zhang, Edwin and Abdrazakov, Linar and Qingyam, Guo and Zhang, Jason and Guo, Zhibin}, booktitle = {Proceedings of the NeurIPS 2022 Competitions Track}, pages = {204--216}, year = {2022}, editor = {Ciccone, Marco and Stolovitzky, Gustavo and Albrecht, Jacob}, volume = {220}, series = {Proceedings of Machine Learning Research}, month = {28 Nov--09 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v220/kiseleva23a/kiseleva23a.pdf}, url = {https://proceedings.mlr.press/v220/kiseleva23a.html}, abstract = {Human intelligence possesses the extraordinary ability to adapt rapidly to new tasks and multi-modal environments. This capacity emerges at an early age, as humans acquire new skills and learn to solve problems by imitating others or following natural language instructions. To facilitate research in this area, we recently hosted the second \emph{IGLU: Interactive Grounded Language Understanding in a Collaborative Environment} competition. The primary objective of the competition is to address the challenge of creating interactive agents that can learn to solve complex tasks by receiving grounded natural language instructions in a collaborative environment. Given the complexity of this challenge, we divided it into two sub-tasks: first, deciding whether the provided grounded instruction requires clarification, and second, following a clear grounded instruction to complete the task description.} }
Endnote
%0 Conference Paper %T Interactive Grounded Language Understanding in a Collaborative Environment: Retrospective on Iglu 2022 Competition %A Julia Kiseleva %A Alexey Skrynnik %A Artem Zholus %A Shrestha Mohanty %A Negar Arabzadeh %A Marc-Alexandre Côté %A Mohammad Aliannejadi %A Milagro Teruel %A Ziming Li %A Mikhail Burtsev %A Maartje ter Hoeve %A Zoya Volovikova %A Aleksandr Panov %A Yuxuan Sun %A Kavya Srinet %A Arthur Szlam %A Ahmed Awadallah %A Seungeun Rho %A Taehwan Kwon %A Daniel Wontae Nam %A Felipe Bivort Haiek %A Edwin Zhang %A Linar Abdrazakov %A Guo Qingyam %A Jason Zhang %A Zhibin Guo %B Proceedings of the NeurIPS 2022 Competitions Track %C Proceedings of Machine Learning Research %D 2022 %E Marco Ciccone %E Gustavo Stolovitzky %E Jacob Albrecht %F pmlr-v220-kiseleva23a %I PMLR %P 204--216 %U https://proceedings.mlr.press/v220/kiseleva23a.html %V 220 %X Human intelligence possesses the extraordinary ability to adapt rapidly to new tasks and multi-modal environments. This capacity emerges at an early age, as humans acquire new skills and learn to solve problems by imitating others or following natural language instructions. To facilitate research in this area, we recently hosted the second \emph{IGLU: Interactive Grounded Language Understanding in a Collaborative Environment} competition. The primary objective of the competition is to address the challenge of creating interactive agents that can learn to solve complex tasks by receiving grounded natural language instructions in a collaborative environment. Given the complexity of this challenge, we divided it into two sub-tasks: first, deciding whether the provided grounded instruction requires clarification, and second, following a clear grounded instruction to complete the task description.
APA
Kiseleva, J., Skrynnik, A., Zholus, A., Mohanty, S., Arabzadeh, N., Côté, M., Aliannejadi, M., Teruel, M., Li, Z., Burtsev, M., ter Hoeve, M., Volovikova, Z., Panov, A., Sun, Y., Srinet, K., Szlam, A., Awadallah, A., Rho, S., Kwon, T., Wontae Nam, D., Bivort Haiek, F., Zhang, E., Abdrazakov, L., Qingyam, G., Zhang, J. & Guo, Z.. (2022). Interactive Grounded Language Understanding in a Collaborative Environment: Retrospective on Iglu 2022 Competition. Proceedings of the NeurIPS 2022 Competitions Track, in Proceedings of Machine Learning Research 220:204-216 Available from https://proceedings.mlr.press/v220/kiseleva23a.html.

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