@inproceedings{zhang-etal-2020-language-embeddings,
title = "Do Language Embeddings capture Scales?",
author = "Zhang, Xikun and
Ramachandran, Deepak and
Tenney, Ian and
Elazar, Yanai and
Roth, Dan",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.439",
doi = "10.18653/v1/2020.findings-emnlp.439",
pages = "4889--4896",
abstract = "Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.",
}
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<abstract>Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.</abstract>
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%0 Conference Proceedings
%T Do Language Embeddings capture Scales?
%A Zhang, Xikun
%A Ramachandran, Deepak
%A Tenney, Ian
%A Elazar, Yanai
%A Roth, Dan
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F zhang-etal-2020-language-embeddings
%X Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge. One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects. We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning. We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.
%R 10.18653/v1/2020.findings-emnlp.439
%U https://aclanthology.org/2020.findings-emnlp.439
%U https://doi.org/10.18653/v1/2020.findings-emnlp.439
%P 4889-4896
Markdown (Informal)
[Do Language Embeddings capture Scales?](https://aclanthology.org/2020.findings-emnlp.439) (Zhang et al., Findings 2020)
ACL
- Xikun Zhang, Deepak Ramachandran, Ian Tenney, Yanai Elazar, and Dan Roth. 2020. Do Language Embeddings capture Scales?. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4889–4896, Online. Association for Computational Linguistics.