TY - GEN
T1 - TextGraphs 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration
AU - Jansen, Peter
AU - Ustalov, Dmitry
N1 - Publisher Copyright:
© COLING 2020.All rights reserved.
PY - 2020
Y1 - 2020
N2 - The 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration tasks participants with regenerating large detailed multi-fact explanations for standardized science exam questions. Given a question, correct answer, and knowledge base, models must rank each fact in the knowledge base such that facts most likely to appear in the explanation are ranked highest. Explanations consist of an average of 6 (and as many as 16) facts that span both core scientific knowledge and world knowledge, and form an explicit lexically-connected “explanation graph” describing how the facts interrelate. In this second iteration of the explanation regeneration shared task, participants are supplied with more than double the training and evaluation data of the first shared task, as well as a knowledge base nearly double in size, both of which expand into more challenging scientific topics that increase the difficulty of the task. In total 10 teams participated, and 5 teams submitted system description papers. The best-performing teams significantly increased state-of-the-art performance both in terms of ranking (mean average precision) and inference speed on this challenge task.
AB - The 2020 Shared Task on Multi-Hop Inference for Explanation Regeneration tasks participants with regenerating large detailed multi-fact explanations for standardized science exam questions. Given a question, correct answer, and knowledge base, models must rank each fact in the knowledge base such that facts most likely to appear in the explanation are ranked highest. Explanations consist of an average of 6 (and as many as 16) facts that span both core scientific knowledge and world knowledge, and form an explicit lexically-connected “explanation graph” describing how the facts interrelate. In this second iteration of the explanation regeneration shared task, participants are supplied with more than double the training and evaluation data of the first shared task, as well as a knowledge base nearly double in size, both of which expand into more challenging scientific topics that increase the difficulty of the task. In total 10 teams participated, and 5 teams submitted system description papers. The best-performing teams significantly increased state-of-the-art performance both in terms of ranking (mean average precision) and inference speed on this challenge task.
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U2 - 10.18653/v1/2020.textgraphs-1.10
DO - 10.18653/v1/2020.textgraphs-1.10
M3 - Conference contribution
AN - SCOPUS:85100460261
T3 - COLING 2020 - Graph-Based Methods for Natural Language Processing - Proceedings of the 14th Workshop, TextGraphs 2020
SP - 85
EP - 97
BT - COLING 2020 - Graph-Based Methods for Natural Language Processing - Proceedings of the 14th Workshop, TextGraphs 2020
A2 - Ustalov, Dmitry
A2 - Somasundaran, Swapna
A2 - Panchenko, Alexander
A2 - Malliaros, Fragkiskos D.
A2 - Hulpus, Ioana
A2 - Jansen, Peter
A2 - Jana, Abhik
PB - Association for Computational Linguistics
T2 - 14th Workshop on Graph-Based Methods for Natural Language Processing, TextGraphs 2020, in conjunction with the 28th International Conference on Computational Linguistics, COLING 2020
Y2 - 13 December 2020
ER -