@inproceedings{emami-etal-2018-knowledge,
title = "A Knowledge Hunting Framework for Common Sense Reasoning",
author = "Emami, Ali and
De La Cruz, Noelia and
Trischler, Adam and
Suleman, Kaheer and
Cheung, Jackie Chi Kit",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1220/",
doi = "10.18653/v1/D18-1220",
pages = "1949--1958",
abstract = "We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge. 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, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable."
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<abstract>We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge. 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, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable.</abstract>
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%0 Conference Proceedings
%T A Knowledge Hunting Framework for Common Sense Reasoning
%A Emami, Ali
%A De La Cruz, Noelia
%A Trischler, Adam
%A Suleman, Kaheer
%A Cheung, Jackie Chi Kit
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F emami-etal-2018-knowledge
%X We introduce an automatic system that achieves state-of-the-art results on the Winograd Schema Challenge (WSC), a common sense reasoning task that requires diverse, complex forms of inference and knowledge. 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, then extracts and classifies knowledge from the returned results and weighs them to make a resolution. Our approach improves F1 performance on the full WSC by 0.21 over the previous best and represents the first system to exceed 0.5 F1. We further demonstrate that the approach is competitive on the Choice of Plausible Alternatives (COPA) task, which suggests that it is generally applicable.
%R 10.18653/v1/D18-1220
%U https://aclanthology.org/D18-1220/
%U https://doi.org/10.18653/v1/D18-1220
%P 1949-1958
Markdown (Informal)
[A Knowledge Hunting Framework for Common Sense Reasoning](https://aclanthology.org/D18-1220/) (Emami et al., EMNLP 2018)
ACL
- Ali Emami, Noelia De La Cruz, Adam Trischler, Kaheer Suleman, and Jackie Chi Kit Cheung. 2018. A Knowledge Hunting Framework for Common Sense Reasoning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1949–1958, Brussels, Belgium. Association for Computational Linguistics.