@inproceedings{vivek-kalyan-etal-2021-textgraphs,
title = "Textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings",
author = "Vivek Kalyan, Sureshkumar and
Witteveen, Sam and
Andrews, Martin",
editor = "Panchenko, Alexander and
Malliaros, Fragkiskos D. and
Logacheva, Varvara and
Jana, Abhik and
Ustalov, Dmitry and
Jansen, Peter",
booktitle = "Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.textgraphs-1.20",
doi = "10.18653/v1/2021.textgraphs-1.20",
pages = "176--180",
abstract = "Creating explanations for answers to science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. This year, to refocus the Textgraphs Shared Task on the problem of gathering relevant statements (rather than solely finding a single {`}correct path{'}), the WorldTree dataset was augmented with expert ratings of {`}relevance{'} of statements to each overall explanation. Our system, which achieved second place on the Shared Task leaderboard, combines initial statement retrieval; language models trained to predict the relevance scores; and ensembling of a number of the resulting rankings. Our code implementation is made available at \url{https://github.com/mdda/worldtree_corpus/tree/textgraphs_2021}",
}
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<abstract>Creating explanations for answers to science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. This year, to refocus the Textgraphs Shared Task on the problem of gathering relevant statements (rather than solely finding a single ‘correct path’), the WorldTree dataset was augmented with expert ratings of ‘relevance’ of statements to each overall explanation. Our system, which achieved second place on the Shared Task leaderboard, combines initial statement retrieval; language models trained to predict the relevance scores; and ensembling of a number of the resulting rankings. Our code implementation is made available at https://github.com/mdda/worldtree_corpus/tree/textgraphs₂021</abstract>
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%0 Conference Proceedings
%T Textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings
%A Vivek Kalyan, Sureshkumar
%A Witteveen, Sam
%A Andrews, Martin
%Y Panchenko, Alexander
%Y Malliaros, Fragkiskos D.
%Y Logacheva, Varvara
%Y Jana, Abhik
%Y Ustalov, Dmitry
%Y Jansen, Peter
%S Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F vivek-kalyan-etal-2021-textgraphs
%X Creating explanations for answers to science questions is a challenging task that requires multi-hop inference over a large set of fact sentences. This year, to refocus the Textgraphs Shared Task on the problem of gathering relevant statements (rather than solely finding a single ‘correct path’), the WorldTree dataset was augmented with expert ratings of ‘relevance’ of statements to each overall explanation. Our system, which achieved second place on the Shared Task leaderboard, combines initial statement retrieval; language models trained to predict the relevance scores; and ensembling of a number of the resulting rankings. Our code implementation is made available at https://github.com/mdda/worldtree_corpus/tree/textgraphs₂021
%R 10.18653/v1/2021.textgraphs-1.20
%U https://aclanthology.org/2021.textgraphs-1.20
%U https://doi.org/10.18653/v1/2021.textgraphs-1.20
%P 176-180
Markdown (Informal)
[Textgraphs-15 Shared Task System Description : Multi-Hop Inference Explanation Regeneration by Matching Expert Ratings](https://aclanthology.org/2021.textgraphs-1.20) (Vivek Kalyan et al., TextGraphs 2021)
ACL