Reasoning about RoboCup Soccer Narratives

Hannaneh Hajishirzi, Julia Hockenmaier, Erik T. Mueller, Eyal Amir
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:329-338, 2011.

Abstract

This paper presents an approach for learning to translate simple narratives, i.e., texts (sequences of sentences) describing dynamic systems, into coherent sequences of events without the need for labeled training data. Our approach incorporates domain knowledge in the form of preconditions and effects of events, and we show that it outperforms state-of-the-art supervised learning systems on the task of reconstructing RoboCup soccer games from their commentaries.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR9-hajishirzi11a, title = {Reasoning about RoboCup Soccer Narratives}, author = {Hajishirzi, Hannaneh and Hockenmaier, Julia and Mueller, Erik T. and Amir, Eyal}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {329--338}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/hajishirzi11a/hajishirzi11a.pdf}, url = {https://proceedings.mlr.press/r9/hajishirzi11a.html}, abstract = {This paper presents an approach for learning to translate simple narratives, i.e., texts (sequences of sentences) describing dynamic systems, into coherent sequences of events without the need for labeled training data. Our approach incorporates domain knowledge in the form of preconditions and effects of events, and we show that it outperforms state-of-the-art supervised learning systems on the task of reconstructing RoboCup soccer games from their commentaries.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Reasoning about RoboCup Soccer Narratives %A Hannaneh Hajishirzi %A Julia Hockenmaier %A Erik T. Mueller %A Eyal Amir %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-hajishirzi11a %I PMLR %P 329--338 %U https://proceedings.mlr.press/r9/hajishirzi11a.html %V R9 %X This paper presents an approach for learning to translate simple narratives, i.e., texts (sequences of sentences) describing dynamic systems, into coherent sequences of events without the need for labeled training data. Our approach incorporates domain knowledge in the form of preconditions and effects of events, and we show that it outperforms state-of-the-art supervised learning systems on the task of reconstructing RoboCup soccer games from their commentaries. %Z Reissued by PMLR on 04 October 2026.
APA
Hajishirzi, H., Hockenmaier, J., Mueller, E.T. & Amir, E.. (2011). Reasoning about RoboCup Soccer Narratives. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:329-338 Available from https://proceedings.mlr.press/r9/hajishirzi11a.html. Reissued by PMLR on 04 October 2026.

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