@inproceedings{prakash-etal-2019-combining,
title = "Combining Knowledge Hunting and Neural Language Models to Solve the {W}inograd Schema Challenge",
author = "Prakash, Ashok and
Sharma, Arpit and
Mitra, Arindam and
Baral, Chitta",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1614",
doi = "10.18653/v1/P19-1614",
pages = "6110--6119",
abstract = "Winograd Schema Challenge (WSC) is a pronoun resolution task which seems to require reasoning with commonsense knowledge. The needed knowledge is not present in the given text. Automatic extraction of the needed knowledge is a bottleneck in solving the challenge. The existing state-of-the-art approach uses the knowledge embedded in their pre-trained language model. However, the language models only embed part of the knowledge, the ones related to frequently co-existing concepts. This limits the performance of such models on the WSC problems. In this work, we build-up on the language model based methods and augment them with a commonsense knowledge hunting (using automatic extraction from text) module and an explicit reasoning module. Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5.53{\%} and 7.7{\%} respectively. Overall our system achieves the state-of-the-art accuracy of 71.06{\%} on the WSC dataset, an improvement of 7.36{\%} over the previous best.",
}
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<abstract>Winograd Schema Challenge (WSC) is a pronoun resolution task which seems to require reasoning with commonsense knowledge. The needed knowledge is not present in the given text. Automatic extraction of the needed knowledge is a bottleneck in solving the challenge. The existing state-of-the-art approach uses the knowledge embedded in their pre-trained language model. However, the language models only embed part of the knowledge, the ones related to frequently co-existing concepts. This limits the performance of such models on the WSC problems. In this work, we build-up on the language model based methods and augment them with a commonsense knowledge hunting (using automatic extraction from text) module and an explicit reasoning module. Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5.53% and 7.7% respectively. Overall our system achieves the state-of-the-art accuracy of 71.06% on the WSC dataset, an improvement of 7.36% over the previous best.</abstract>
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%0 Conference Proceedings
%T Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge
%A Prakash, Ashok
%A Sharma, Arpit
%A Mitra, Arindam
%A Baral, Chitta
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F prakash-etal-2019-combining
%X Winograd Schema Challenge (WSC) is a pronoun resolution task which seems to require reasoning with commonsense knowledge. The needed knowledge is not present in the given text. Automatic extraction of the needed knowledge is a bottleneck in solving the challenge. The existing state-of-the-art approach uses the knowledge embedded in their pre-trained language model. However, the language models only embed part of the knowledge, the ones related to frequently co-existing concepts. This limits the performance of such models on the WSC problems. In this work, we build-up on the language model based methods and augment them with a commonsense knowledge hunting (using automatic extraction from text) module and an explicit reasoning module. Our end-to-end system built in such a manner improves on the accuracy of two of the available language model based approaches by 5.53% and 7.7% respectively. Overall our system achieves the state-of-the-art accuracy of 71.06% on the WSC dataset, an improvement of 7.36% over the previous best.
%R 10.18653/v1/P19-1614
%U https://aclanthology.org/P19-1614
%U https://doi.org/10.18653/v1/P19-1614
%P 6110-6119
Markdown (Informal)
[Combining Knowledge Hunting and Neural Language Models to Solve the Winograd Schema Challenge](https://aclanthology.org/P19-1614) (Prakash et al., ACL 2019)
ACL