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 | 1165 | 2019 |
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 | 962 | 2016 |
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 | 817 | 2022 |
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 | 592 | 2018 |
Targeted Syntactic Evaluation of Language Models R Marvin, T Linzen Proceedings of the 2018 Conference on Empirical Methods in Natural Language …, 2018 | 428 | 2018 |
COGS: A Compositional Generalization Challenge Based on Semantic Interpretation N Kim, T Linzen EMNLP, 2020 | 242 | 2020 |
Syntactic Structure from Deep Learning T Linzen, M Baroni Annual Reviews of Linguistics, 2021 | 202 | 2021 |
Issues in evaluating semantic spaces using word analogies T Linzen Proceedings of the First Workshop on Evaluating Vector Space Representations …, 2016 | 186 | 2016 |
How Can We Accelerate Progress Towards Human-like Linguistic Generalization? T Linzen Proceedings of the 58th Annual Meeting of the Association for Computational …, 2020 | 179 | 2020 |
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 | 163 | 2020 |
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 | 158 | 2019 |
Uncertainty and expectation in sentence processing: evidence from subcategorization distributions T Linzen, TF Jaeger Cognitive Science 40 (6), 1382-1411, 2016 | 129 | 2016 |
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 | 126 | 2019 |
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 | 105 | 2020 |
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 | 104 | 2019 |
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 | 98 | 2018 |
Quantity doesn't buy quality syntax with neural language models M van Schijndel, A Mueller, T Linzen EMNLP 2019, 2019 | 94 | 2019 |
How much do language models copy from their training data? evaluating linguistic novelty in text generation using raven RT McCoy, P Smolensky, T Linzen, J Gao, A Celikyilmaz Transactions of the Association for Computational Linguistics 11, 652-670, 2023 | 87 | 2023 |
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 | 87 | 2018 |
The MultiBERTs: BERT Reproductions for Robustness Analysis T Sellam, S Yadlowsky, J Wei, N Saphra, A D'Amour, T Linzen, J Bastings, ... ICLR 2022, 2021 | 83 | 2021 |