@inproceedings{sahu-etal-2021-comprehension,
title = "Comprehension Based Question Answering using Bloom{'}s Taxonomy",
author = "Sahu, Pritish and
Cogswell, Michael and
Divakaran, Ajay and
Rutherford-Quach, Sara",
editor = "Rogers, Anna and
Calixto, Iacer and
Vuli{\'c}, Ivan and
Saphra, Naomi and
Kassner, Nora and
Camburu, Oana-Maria and
Bansal, Trapit and
Shwartz, Vered",
booktitle = "Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.repl4nlp-1.3",
doi = "10.18653/v1/2021.repl4nlp-1.3",
pages = "20--28",
abstract = "Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom{'}s Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions. We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.",
}
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<abstract>Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom’s Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions. We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.</abstract>
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%0 Conference Proceedings
%T Comprehension Based Question Answering using Bloom’s Taxonomy
%A Sahu, Pritish
%A Cogswell, Michael
%A Divakaran, Ajay
%A Rutherford-Quach, Sara
%Y Rogers, Anna
%Y Calixto, Iacer
%Y Vulić, Ivan
%Y Saphra, Naomi
%Y Kassner, Nora
%Y Camburu, Oana-Maria
%Y Bansal, Trapit
%Y Shwartz, Vered
%S Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F sahu-etal-2021-comprehension
%X Current pre-trained language models have lots of knowledge, but a more limited ability to use that knowledge. Bloom’s Taxonomy helps educators teach children how to use knowledge by categorizing comprehension skills, so we use it to analyze and improve the comprehension skills of large pre-trained language models. Our experiments focus on zero-shot question answering, using the taxonomy to provide proximal context that helps the model answer questions by being relevant to those questions. We show targeting context in this manner improves performance across 4 popular common sense question answer datasets.
%R 10.18653/v1/2021.repl4nlp-1.3
%U https://aclanthology.org/2021.repl4nlp-1.3
%U https://doi.org/10.18653/v1/2021.repl4nlp-1.3
%P 20-28
Markdown (Informal)
[Comprehension Based Question Answering using Bloom’s Taxonomy](https://aclanthology.org/2021.repl4nlp-1.3) (Sahu et al., RepL4NLP 2021)
ACL