@inproceedings{gupta-srinivasaraghavan-2020-explanation,
title = "Explanation Regeneration via Multi-Hop {ILP} Inference over Knowledge Base",
author = "Gupta, Aayushee and
Srinivasaraghavan, Gopalakrishnan",
editor = "Ustalov, Dmitry and
Somasundaran, Swapna and
Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Hulpu{\textcommabelow{s}}, Ioana and
Jansen, Peter and
Jana, Abhik",
booktitle = "Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.textgraphs-1.13",
doi = "10.18653/v1/2020.textgraphs-1.13",
pages = "109--114",
abstract = "Textgraphs 2020 Workshop organized a shared task on {`}Explanation Regeneration{'} that required reconstructing gold explanations for elementary science questions. This work describes our submission to the task which is based on multiple components: a BERT baseline ranking, an Integer Linear Program (ILP) based re-scoring and a regression model for re-ranking the explanation facts. Our system achieved a Mean Average Precision score of 0.3659.",
}
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<abstract>Textgraphs 2020 Workshop organized a shared task on ‘Explanation Regeneration’ that required reconstructing gold explanations for elementary science questions. This work describes our submission to the task which is based on multiple components: a BERT baseline ranking, an Integer Linear Program (ILP) based re-scoring and a regression model for re-ranking the explanation facts. Our system achieved a Mean Average Precision score of 0.3659.</abstract>
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%0 Conference Proceedings
%T Explanation Regeneration via Multi-Hop ILP Inference over Knowledge Base
%A Gupta, Aayushee
%A Srinivasaraghavan, Gopalakrishnan
%Y Ustalov, Dmitry
%Y Somasundaran, Swapna
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Hulpu\textcommabelows, Ioana
%Y Jansen, Peter
%Y Jana, Abhik
%S Proceedings of the Graph-based Methods for Natural Language Processing (TextGraphs)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F gupta-srinivasaraghavan-2020-explanation
%X Textgraphs 2020 Workshop organized a shared task on ‘Explanation Regeneration’ that required reconstructing gold explanations for elementary science questions. This work describes our submission to the task which is based on multiple components: a BERT baseline ranking, an Integer Linear Program (ILP) based re-scoring and a regression model for re-ranking the explanation facts. Our system achieved a Mean Average Precision score of 0.3659.
%R 10.18653/v1/2020.textgraphs-1.13
%U https://aclanthology.org/2020.textgraphs-1.13
%U https://doi.org/10.18653/v1/2020.textgraphs-1.13
%P 109-114
Markdown (Informal)
[Explanation Regeneration via Multi-Hop ILP Inference over Knowledge Base](https://aclanthology.org/2020.textgraphs-1.13) (Gupta & Srinivasaraghavan, TextGraphs 2020)
ACL