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Tal Linzen
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Cited by
Year
Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies
T Linzen, E Dupoux, Y Goldberg
Transactions of the Association for Computational Linguistics 4, 521-535, 2016
7762016
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
RT McCoy, E Pavlick, T Linzen
Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019
7542019
Colorless green recurrent networks dream hierarchically
K Gulordava, P Bojanowski, E Grave, T Linzen, M Baroni
Proceedings of the 16th Annual Conference of the North American Chapter of …, 2018
4612018
Targeted Syntactic Evaluation of Language Models
R Marvin, T Linzen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018
3042018
Issues in evaluating semantic spaces using word analogies
T Linzen
Proceedings of the First Workshop on Evaluating Vector Space Representations …, 2016
1562016
COGS: A Compositional Generalization Challenge Based on Semantic Interpretation
N Kim, T Linzen
EMNLP, 2020
1122020
Uncertainty and expectation in sentence processing: evidence from subcategorization distributions
T Linzen, TF Jaeger
Cognitive Science 40 (6), 1382-1411, 2016
1112016
How Can We Accelerate Progress Towards Human-like Linguistic Generalization?
T Linzen
Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020
1102020
Syntactic Structure from Deep Learning
T Linzen, M Baroni
Annual Reviews of Linguistics, 2021
1062021
BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance
RT McCoy, J Min, T Linzen
Proceedings of BlackboxNLP 2020, 2019
972019
Syntactic Data Augmentation Increases Robustness to Inference Heuristics
J Min, RT McCoy, D Das, E Pitler, T Linzen
Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020
962020
Human few-shot learning of compositional instructions
BM Lake, T Linzen, M Baroni
Proceedings of the 41st Annual Conference of the Cognitive Science Society, 2019
782019
Probing What Different NLP Tasks Teach Machines about Function Word Comprehension
N Kim, R Patel, A Poliak, A Wang, P Xia, RT McCoy, I Tenney, A Ross, ...
arXiv preprint arXiv:1904.11544, 2019
762019
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
692022
In Spoken Word Recognition, the Future Predicts the Past
L Gwilliams, T Linzen, D Poeppel, A Marantz
Journal of Neuroscience 38 (35), 7585-7599, 2018
692018
Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks
RT McCoy, R Frank, T Linzen
Proceedings of the 40th Annual Conference of the Cognitive Science Society, 2018
682018
Quantity doesn't buy quality syntax with neural language models
M van Schijndel, A Mueller, T Linzen
EMNLP 2019, 2019
642019
Does syntax need to grow on trees? Sources of hierarchical inductive bias in sequence-to-sequence networks
RT McCoy, R Frank, T Linzen
Transactions of the Association for Computational Linguistics 8, 125--140, 2020
622020
The role of morphology in phoneme prediction: Evidence from MEG
A Ettinger, T Linzen, A Marantz
Brain and Language 129, 14-23, 2014
602014
A Neural Model of Adaptation in Reading
M van Schijndel, T Linzen
Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018
592018
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Articles 1–20