OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Comparing Character-level Neural Language Models Using a Lexical Decision Task
Gaël Le Godais, Tal Linzen, Emmanuel Dupoux
(2017), pp. 125-130
Open Access | Times Cited: 19

Showing 19 citing articles:

LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better
Adhiguna Kuncoro, Chris Dyer, John Hale, et al.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2018), pp. 1426-1436
Open Access | Times Cited: 159

Simulating Early Phonetic and Word Learning Without Linguistic Categories
Marvin Lavechin, Maureen de Seyssel, Hadrien Titeux, et al.
Developmental Science (2025) Vol. 28, Iss. 2
Closed Access

Exploring the Syntactic Abilities of RNNs with Multi-task Learning
Émile Enguehard, Yoav Goldberg, Tal Linzen
(2017), pp. 3-14
Open Access | Times Cited: 32

BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models
Marvin Lavechin, Yaya Sy, Hadrien Titeux, et al.
Interspeech 2022 (2023)
Open Access | Times Cited: 8

Can statistical learning bootstrap early language acquisition? A modeling investigation
Marvin Lavechin, Maureen de Seyssel, Hadrien Titeux, et al.
(2022)
Open Access | Times Cited: 12

The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling
Tu Anh Nguyen, Maureen de Seyssel, Patricia Rozé, et al.
HAL (Le Centre pour la Communication Scientifique Directe) (2020)
Open Access | Times Cited: 16

Information Retrieval for ZeroSpeech 2021: The Submission by University of Wroclaw
Jan Chorowski, Grzegorz Ciesielski, Jarosław Dzikowski, et al.
Interspeech 2022 (2021), pp. 971-975
Open Access | Times Cited: 10

Linguistic Resources for Bhojpuri, Magahi, and Maithili: Statistics about Them, Their Similarity Estimates, and Baselines for Three Applications
Rajesh Kumar Mundotiya, Manish Kumar Singh, Rahul Kapur, et al.
ACM Transactions on Asian and Low-Resource Language Information Processing (2021) Vol. 20, Iss. 6, pp. 1-37
Open Access | Times Cited: 8

Exploring the Syntactic Abilities of RNNs with Multi-task Learning
Émile Enguehard, Yoav Goldberg, Tal Linzen
arXiv (Cornell University) (2017)
Closed Access | Times Cited: 6

Basic Linguistic Resources and Baselines for Bhojpuri, Magahi and Maithili for Natural Language Processing
Rajesh Kumar Mundotiya, Manish Kumar Singh, Rahul Kapur, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 4

An Exploration of Hubert with Large Number of Cluster Units and Model Assessment Using Bayesian Information Criterion
Takashi Maekaku, Xuankai Chang, Yuya Fujita, et al.
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022), pp. 7107-7111
Closed Access | Times Cited: 3

Representation Learning With Hidden Unit Clustering for Low Resource Speech Applications
Varun Krishna, Tarun Sai, Sriram Ganapathy
IEEE/ACM Transactions on Audio Speech and Language Processing (2023) Vol. 32, pp. 1036-1047
Open Access | Times Cited: 1

Self Supervised Representation Learning with Deep Clustering for Acoustic Unit Discovery from Raw Speech
Varun Krishna, Sriram Ganapathy
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022), pp. 3268-3272
Closed Access | Times Cited: 2

Linguistic Resources for Bhojpuri, Magahi and Maithili: Statistics about them, their Similarity Estimates, and Baselines for Three Applications
Rajesh Kumar Mundotiya, Manish Kumar Singh, Rahul Kapur, et al.
(2020)
Closed Access | Times Cited: 1

Pseudo-Label Based Supervised Contrastive Loss for Robust Speech Representations
Varun Krishna, Sriram Ganapathy
2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) (2023) Vol. 33, pp. 1-8
Closed Access

Information Retrieval for ZeroSpeech 2021: The Submission by University of Wroclaw
Jan Chorowski, Grzegorz Ciesielski, Jarosław Dzikowski, et al.
arXiv (Cornell University) (2021)
Open Access

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