@inproceedings{emami-etal-2018-generalized,
title = "A Generalized Knowledge Hunting Framework for the {W}inograd Schema Challenge",
author = "Emami, Ali and
Trischler, Adam and
Suleman, Kaheer and
Cheung, Jackie Chi Kit",
editor = "Cordeiro, Silvio Ricardo and
Oraby, Shereen and
Pavalanathan, Umashanthi and
Rim, Kyeongmin",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Student Research Workshop",
month = jun,
year = "2018",
address = "New Orleans, Louisiana, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-4004",
doi = "10.18653/v1/N18-4004",
pages = "25--31",
abstract = "We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA). Problem instances from these tasks require diverse, complex forms of inference and knowledge to solve. Our method uses a knowledge-hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine. It extracts and classifies knowledge from the returned results and weighs it to make a resolution. Our approach improves F1 performance on the WSC by 0.16 over the previous best and is competitive with the state-of-the-art on COPA, demonstrating its general applicability.",
}
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<abstract>We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA). Problem instances from these tasks require diverse, complex forms of inference and knowledge to solve. Our method uses a knowledge-hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine. It extracts and classifies knowledge from the returned results and weighs it to make a resolution. Our approach improves F1 performance on the WSC by 0.16 over the previous best and is competitive with the state-of-the-art on COPA, demonstrating its general applicability.</abstract>
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%0 Conference Proceedings
%T A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge
%A Emami, Ali
%A Trischler, Adam
%A Suleman, Kaheer
%A Cheung, Jackie Chi Kit
%Y Cordeiro, Silvio Ricardo
%Y Oraby, Shereen
%Y Pavalanathan, Umashanthi
%Y Rim, Kyeongmin
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F emami-etal-2018-generalized
%X We introduce an automatic system that performs well on two common-sense reasoning tasks, the Winograd Schema Challenge (WSC) and the Choice of Plausible Alternatives (COPA). Problem instances from these tasks require diverse, complex forms of inference and knowledge to solve. Our method uses a knowledge-hunting module to gather text from the web, which serves as evidence for candidate problem resolutions. Given an input problem, our system generates relevant queries to send to a search engine. It extracts and classifies knowledge from the returned results and weighs it to make a resolution. Our approach improves F1 performance on the WSC by 0.16 over the previous best and is competitive with the state-of-the-art on COPA, demonstrating its general applicability.
%R 10.18653/v1/N18-4004
%U https://aclanthology.org/N18-4004
%U https://doi.org/10.18653/v1/N18-4004
%P 25-31
Markdown (Informal)
[A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge](https://aclanthology.org/N18-4004) (Emami et al., NAACL 2018)
ACL
- Ali Emami, Adam Trischler, Kaheer Suleman, and Jackie Chi Kit Cheung. 2018. A Generalized Knowledge Hunting Framework for the Winograd Schema Challenge. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop, pages 25–31, New Orleans, Louisiana, USA. Association for Computational Linguistics.