@inproceedings{jain-singh-2019-karna,
title = "{KARNA} at {COIN} Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense",
author = "Jain, Yash and
Singh, Chinmay",
editor = "Ostermann, Simon and
Zhang, Sheng and
Roth, Michael and
Clark, Peter",
booktitle = "Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6008",
doi = "10.18653/v1/D19-6008",
pages = "75--79",
abstract = "This paper describes our model for COmmonsense INference in Natural Language Processing (COIN) shared task 1: Commonsense Inference in Everyday Narrations. This paper explores the use of Bidirectional Encoder Representations from Transformers(BERT) along with external relational knowledge from ConceptNet to tackle the problem of commonsense inference. The input passage, question, and answer are augmented with relational knowledge from ConceptNet. Using this technique we are able to achieve an accuracy of 73.3 {\%} on the official test data.",
}
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%0 Conference Proceedings
%T KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense
%A Jain, Yash
%A Singh, Chinmay
%Y Ostermann, Simon
%Y Zhang, Sheng
%Y Roth, Michael
%Y Clark, Peter
%S Proceedings of the First Workshop on Commonsense Inference in Natural Language Processing
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F jain-singh-2019-karna
%X This paper describes our model for COmmonsense INference in Natural Language Processing (COIN) shared task 1: Commonsense Inference in Everyday Narrations. This paper explores the use of Bidirectional Encoder Representations from Transformers(BERT) along with external relational knowledge from ConceptNet to tackle the problem of commonsense inference. The input passage, question, and answer are augmented with relational knowledge from ConceptNet. Using this technique we are able to achieve an accuracy of 73.3 % on the official test data.
%R 10.18653/v1/D19-6008
%U https://aclanthology.org/D19-6008
%U https://doi.org/10.18653/v1/D19-6008
%P 75-79
Markdown (Informal)
[KARNA at COIN Shared Task 1: Bidirectional Encoder Representations from Transformers with relational knowledge for machine comprehension with common sense](https://aclanthology.org/D19-6008) (Jain & Singh, 2019)
ACL