---
title: "Who cites How Pre-trained Word — 5 citing papers"
description: "5 papers of the Elamur Research library cite «How Pre-trained Word Representations Capture Commonsense Physical Comparisons», found…"
canonical: "https://research.elamur.ai/free/en/papers/goel-2019-how-pre-trained-word-representations/cited-by/"
---
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# Who cites «How Pre-trained Word Representations Capture Commonsense Physical Comparisons»

5 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «How Pre-trained Word Representations Capture Commonsense Physical Comparisons»](https://md.research.elamur.ai/free/en/papers/goel-2019-how-pre-trained-word-representations/)

1. **[Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-Trained Language Models](https://md.research.elamur.ai/free/en/papers/lin-2020-birds-have-four-legs-numersense/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, et al.*2020 · 218 citations*
2. **[Representing Numbers in NLP: a Survey and a Vision](https://md.research.elamur.ai/free/en/papers/thawani-2021-representing-numbers-nlp-survey-vision/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Avijit Thawani, Jay Pujara, Filip Ilievski, et al.*2021 · 139 citations*
3. **[PROST: Physical Reasoning about Objects through Space and Time](https://md.research.elamur.ai/free/en/papers/ouellette-2021-prost-physical-reasoning-about-objects/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Stéphane Aroca-Ouellette, Cory Paik, Alessandro Roncone, et al.*2021 · 40 citations*

2 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/goel-2019-how-pre-trained-word-representations/) — How Pre-trained Word Representations Capture Commonsense Physical Comparisons