Targeted Syntactic Evaluation of Language Models

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

Finding structure in time

Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212

Recurrent neural network based language model

Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390

SRILM - an extensible language modeling toolkit

Stolcke, A. (2002) · International Conference on Spoken Language Processing (ICSLP) · doi:10.21437/icslp.2002-303 · Cited by 4,385

Neural Network Acceptability Judgments

Warstadt, A., Singh, A., & Bowman, S. R. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00290 · Cited by 1,215

Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies

Linzen, T., Emmanuel Dupoux, A., & Goldberg, Y. (2016) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00115 · Cited by 820

Agreement attraction in comprehension: Representations and processes

WAGERS, M. W., Lau, E. F., & Phillips, C. (2009) · Journal of Memory and Language · doi:10.1016/j.jml.2009.04.002 · Cited by 639

Broken agreement

Bock, K., & Miller, C. (1991) · Cognitive Psychology · doi:10.1016/0010-0285(91)90003-7 · Cited by 558

Colorless Green Recurrent Networks Dream Hierarchically

Gulordava, K., Bojanowski, P., Grave, É., Linzen, T., & Baroni, M. (2018) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n18-1108 · Cited by 538

What Makes My Model Perplexed? A Linguistic Investigation on Neural Language Models Perplexity

Miaschi, A., Brunato⋄, D., Dell’Orletta⋄, F., & Venturi⋄, G. (2021) · Workshop on Knowledge Extraction and Integration for Deep Learning Architectures: Deep Learning Inside Out (DeeLIO) · doi:10.18653/v1/2021.deelio-1.5 · Cited by 23

Untitled work

(2022) · doi:10.18653/v1/2022.emnlp-main.746 · Cited by 21

DEMETR: Diagnosing Evaluation Metrics for Translation

Karpinska, M., Raj, N., Thai, K., Song, Y., Gupta, A., & Iyyer, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.649 · Cited by 22

Scalable Syntax-Aware Language Models Using Knowledge Distillation

Kuncoro, A., Dyer, C., Rimell, L., Clark, S. C., & Blunsom, P. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1337 · Cited by 26

DirectProbe: Studying Representations without Classifiers

Zhou, Y., & Srikumar, V. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.401 · Cited by 24

Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments.Preprint

Warstadt, A., & Bowman, S. R. (2019) · arXiv (Cornell University) · Cited by 19

RuCoLA: Russian Corpus of Linguistic Acceptability

Mikhailov, V., Shamardina, T., Ryabinin, M., Pestova, A., Smurov, I., & Artemova, E. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.348 · Cited by 22

The Low-Dimensional Linear Geometry of Contextualized Word Representations

Hernandez, E. J., & Andreas, J. (2021) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2021.conll-1.7 · Cited by 32

Modeling rapid language learning by distilling Bayesian priors into artificial neural networks

McCoy, R. T., & Griffiths, T. L. (2025) · Nature Communications · doi:10.1038/s41467-025-59957-y · Cited by 50

Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization

Chen, Y., Liu, P., & Qiu, X. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.179 · Cited by 21

What Don't RNN Language Models Learn About Filler-Gap Dependencies?

Chaves, R. P. (2020) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/f7yj-1n62 · Cited by 18

Effective Batching for Recurrent Neural Network Grammars

Noji, H., & Oseki, Y. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.380 · Cited by 19

Evaluating Document Coherence Modeling

Shen, A., Mistica, M., Salehi, B., Li, H., Baldwin, T., & Qi, J. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00388 · Cited by 22

Linguistic Analysis of Pretrained Sentence Encoders with Acceptability JudgmentsPreprint

Warstadt, A., & Bowman, S. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.03438 · Cited by 38

Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

(2019) · doi:10.18653/v1/d19-62 · Cited by 17

When a sentence does not introduce a discourse entity, Transformer-based models still sometimes refer to it

Schuster, S., & Linzen, T. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.71 · Cited by 18

Visual Comparison of Language Model Adaptation

Sevastjanova, R., Cakmak, E., Ravfogel, S., Cotterell, R., & El‐Assady, M. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209458 · Cited by 17

Acceptability Judgements via Examining the Topology of Attention Maps

Cherniavskii, D., Tulchinskii, E., Mikhailov, V., Proskurina, I., Kushnareva, L., Artemova, E., Barannikov, S., Piontkovskaya, I., Piontkovski, D., & Burnaev, E. V. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.7 · Cited by 21

COMPS: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models

Misra, K., Rayz, J. T., & Ettinger, A. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.213 · Cited by 32

Towards Zero-shot Language Modeling

Ponti, E. M., Vulić, I., Cotterell, R., Reichart, R., & Korhonen, A. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1288 · Cited by 16

Higher-order Comparisons of Sentence Encoder Representations

vqc439, V., Kulmizev, A., Hill, F., Low, D. M., & Søgaard, A. (2019) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 16

BERTs of a feather do not generalize together: Large variability in generalization across models with similar test set performance

McCoy, R. T., Min, J., & Linzen, T. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4110592 · Cited by 19

Language and learning: the cognitive revolution at 60‐odd

Bolhuis, J. J., Crain, S., & Roberts, I. (2023) · Biological reviews/Biological reviews of the Cambridge Philosophical Society · doi:10.1111/brv.12936 · Cited by 19

How Reliable are Model Diagnostics?

Aribandi, V., Tay, Y., & Metzler, D. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.155 · Cited by 15

Explaining Contextualization in Language Models using Visual Analytics

Sevastjanova, R., Kalouli, A.-L., Beck, C., Schäfer, H., & El‐Assady, M. (2021) · doi:10.18653/v1/2021.acl-long.39 · Cited by 16

Can Small and Synthetic Benchmarks Drive Modeling Innovation? A Retrospective Study of Question Answering Modeling Approaches.Preprint

Liu, N. F., Lee, T., Jia, R., & Liang, P. (2021) · arXiv (Cornell University) · Cited by 17

The Rise of Thinking Machines: A Review of Artificial Intelligence in Contemporary Communication

Gholami, M. J., & Abdwani, T. A. R. A. (2024) · Journal of Business Communication & Technology · doi:10.56632/bct.2024.3103 · Cited by 19

Mathematical Structure of Syntactic Merge

Marcolli, M., Chomsky, N., & Berwick, R. C. (2025) · The MIT Press eBooks · doi:10.7551/mitpress/15811.001.0001 · Cited by 27

An Analysis of the Utility of Explicit Negative Examples to Improve the Syntactic Abilities of Neural Language Models

Noji, H., & Takamura, H. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.309 · Cited by 18

The Non-native Speaker Aspect: Indian English in Social Media

Sarkar, R., Mahinder, S., & KhudaBukhsh, A. R. (2020) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/2020.wnut-1.9 · Cited by 14

Schrödinger's tree—On syntax and neural language models

Kulmizev, A., & Nivre, J. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.796788 · Cited by 20

Data-driven Cross-lingual Syntax: An Agreement Study with Massively Multilingual Models

de Varda, A. G., & Marelli, M. (2023) · Computational Linguistics · doi:10.1162/coli_a_00472 · Cited by 19

Sensitivity as a Complexity Measure for Sequence Classification Tasks

Hahn, M., Jurafsky, D., & Futrell, R. (2021) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00403 · Cited by 16

Surprisal does not explain syntactic disambiguation difficulty: evidence from a large-scale benchmarkPreprint

Huang, K., Arehalli, S., Kugemoto, M., Muxica, C., Prasad, G., Dillon, B., & Linzen, T. (2023) · doi:10.31234/osf.io/z38u6 · Cited by 14

How Abstract Is Linguistic Generalization in Large Language Models? Experiments with Argument Structure

Wilson, M., Petty, J., & Frank, R. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00608 · Cited by 14

LMFingerprints: Visual Explanations of Language Model Embedding Spaces through Layerwise Contextualization Scores

Sevastjanova, R., Kalouli, A.-L., Beck, C., Hauptmann, H., & El‐Assady, M. (2022) · Computer Graphics Forum · doi:10.1111/cgf.14541 · Cited by 18

Pretraining Without Attention

Wang, J., Yan, J., Gu, A., & Rush, A. M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.5 · Cited by 57

Simple syntactic rules through rapid synaptic changesPreprint

Sun, L., & Manohar, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.21.572018 · Cited by 13

Representation biases in sentence transformers

Nikolaev, D., & Padó, S. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.268 · Cited by 26

Study on BERT Model for Hate Speech Detection

Gupta, S., Lakra, S., & Kaur, M. (2020) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca49313.2020.9297560 · Cited by 13

Hierarchy in language interpretation: evidence from behavioural experiments and computational modelling

Coopmans, C. W., de Hoop, H., Kaushik, K., Peter Hagoort, A., & Martin, A. E. (2021) · Language Cognition and Neuroscience · doi:10.1080/23273798.2021.1980595 · Cited by 14

How Suitable Are Subword Segmentation Strategies for Translating Non-Concatenative Morphology?

Amrhein, C., & Sennrich, R. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.60 · Cited by 12

Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models

Wilcox, E., Qian, P., Futrell, R., Kohita, R., Lévy, R., & Ballesteros, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.375 · Cited by 15

Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic StatePreprint

Futrell, R., Wilcox, E., Morita, T., Qian, P., Ballesteros, M., & Lévy, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.03260 · Cited by 46

Does BERT agree? Evaluating knowledge of structure dependence through agreement relationsPreprint

Bacon, G., & Regier, T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.09892 · Cited by 19

This is a BERT. Now there are several of them. Can they generalize to novel words?

Haley, C. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.31 · Cited by 11

A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs

Hartmann, M., de Lhoneux, M., Hershcovich, D., Kementchedjhieva, Y., Nielsen, L., Qiu, C., & Søgaard, A. (2021) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2021.conll-1.19 · Cited by 12

Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement

Li, B., Wisniewski, G., & Crabbé, B. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00531 · Cited by 12

How to Plant Trees in Language Models: Data and Architectural Effects on the Emergence of Syntactic Inductive Biases

Mueller, A., & Linzen, T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.629 · Cited by 20

Probing GPT-3’s Linguistic Knowledge on Semantic Tasks

Zhang, L., Wang, M., Chen, L., & Zhang, W. (2022) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2022.blackboxnlp-1.24 · Cited by 13

Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text

Sergeeva, E., Zhu, H., Tahmasebi, A. M., & Szolovits, P. (2019) · International Workshop on Health Text Mining and Information Analysis (LOUHI) · doi:10.18653/v1/d19-6221 · Cited by 11

Assessing the ability of Transformer-based Neural Models to represent structurally unbounded dependencies

Costa, J. K. D., & Chaves, R. P. (2020) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/3sb6-4g20 · Cited by 11

A Closer Look at the Performance of Neural Language Models on Reflexive Anaphor Licensing

Hu, J., Chen, S. Y., & Lévy, R. (2020) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/67qw-mf84 · Cited by 9

Transformers Generalize LinearlyPreprint

Petty, J., & Frank, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.12036 · Cited by 21

Perceptual Structure in the absence of grounding: the impact of abstractedness and subjectivity in color language for LLMs

Loyola, P., Marrese-Taylor, E., & Hoyos-Idrobo, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.102 · Cited by 9

Bigger is not always better: The importance of human-scale language modeling for psycholinguistics

Wilcox, E. G., Hu, M. Y., Mueller, A., Warstadt, A., Choshen, L., Zhuang, C., Williams, A., Cotterell, R., & Linzen, T. (2025) · Journal of Memory and Language · doi:10.1016/j.jml.2025.104650 · Cited by 9

JBLiMP: Japanese Benchmark of Linguistic Minimal Pairs

Someya, T., & Oseki, Y. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.117 · Cited by 9

Overestimation of Syntactic Representation in Neural Language Models

Kodner, J., & Gupta, N. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.160 · Cited by 13

Do Grammatical Error Correction Models Realize Grammatical Generalization?

Mita, M., & Yanaka, H. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.399 · Cited by 8

Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs

Lepori, M. A., Linzen, T., & McCoy, R. T. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.303 · Cited by 10

Word Frequency Does Not Predict Grammatical Knowledge in Language Models

Yu, C., Sie, R., Tedeschi, N., & Bergen, L. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.331 · Cited by 10

Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models

Beyer, A., Loáiciga, S., & Schlangen, D. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.328 · Cited by 12

SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics

Yanaka, H., Mineshima, K., & Inui, K. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.10 · Cited by 10

Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation

Liu, Z., & Prud’hommeaux, E. T. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00467 · Cited by 9

What can linguistics and deep learning contribute to each other?Preprint

Linzen, T. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.04179 · Cited by 8

Evaluating Neural Language Models as Cognitive Models of Language Acquisition

Martínez, H. J. V., Heuser, A., Yang, C. D., & Kodner, J. (2023) · GenBench Workshop on Generalisation (Benchmarking) in NLP (GenBench) · doi:10.18653/v1/2023.genbench-1.4 · Cited by 10

diagNNose: A Library for Neural Activation Analysis

Jumelet, J. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.32 · Cited by 6

Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study

An, A., Qian, P., Wilcox, E., & Lévy, R. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1287 · Cited by 10

A multilabel approach to morphosyntactic probing

Shapiro, N. T., Paullada, A., & Steinert‐Threlkeld, S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.382 · Cited by 10

Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain

Liu, X., Zhou, M., Shi, G., Du, Y., Zhao, L., Wu, Z., Liu, D., Liu, T., & Hu, X. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i7.26068 · Cited by 8

Assessing Grammatical Correctness in Language Learning

Katinskaia, A., & Yangarber, R. (2021) · Työväentutkimus Vuosikirja · Cited by 7

The acceptability and validity of AI-generated psycholinguistic stimuli

Alzahrani, A. A. (2025) · Heliyon · doi:10.1016/j.heliyon.2025.e42083 · Cited by 6

Probing for Incremental Parse States in Autoregressive Language Models

Eisape, T., Gangireddy, V., Lévy, R., & Kim, Y. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.203 · Cited by 11

Understanding Large Language Models : Towards Rigorous and Targeted Interpretability Using Probing Classifiers and Self-Rationalisation

Kunz, J. (2024) · Linköping studies in science and technology. Dissertations · doi:10.3384/9789180754712 · Cited by 7

Characterizing English Preposing in PP constructions

Potts, C. (2024) · Journal of Linguistics · doi:10.1017/s0022226724000227 · Cited by 9

Multiple Admissibility: Judging Grammaticality using Unlabeled Data in Language Learning

Katinskaia, A., & Ivanova, S. (2019) · Workshop on Balto-Slavic Natural Language Processing (BSNLP) · doi:10.18653/v1/w19-3702 · Cited by 6

Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement

Li, B., Wisniewski, G., & Crabbé, B. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.377 · Cited by 6

Language model acceptability judgements are not always robust to context

Sinha, K., Gauthier, J., Mueller, A., Misra, K., Fuentes, K., Lévy, R., & Williams, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.333 · Cited by 12

Beyond the limitations of any imaginable mechanism: Large language models and psycholinguistics

Houghton, C. J., Kazanina, N., & Sukumaran, P. (2023) · Behavioral and Brain Sciences · doi:10.1017/s0140525x23001693 · Cited by 12

Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns

Rabinovich, E., Watson, J., Beekhuizen, B., & Stevenson, S. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1008 · Cited by 5

Evaluating the Morphosyntactic Well-formedness of Generated Texts

Pratapa, A., Anastasopoulos, A., Rijhwani, S., Chaudhary, A., Mortensen, D. R., Neubig, G., & Tsvetkov, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.570 · Cited by 5

BERT-Proof Syntactic Structures: Investigating Errors in Discontinuous Constituency Parsing

Coavoux, M. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.288 · Cited by 7

LLM Explainability

Arous, I., Chehbouni, K., Cheng, Z., & Dossou, B. F. P. (2025) · Springer eBooks · doi:10.1007/978-981-97-8440-0_85-1 · Cited by 5

Finding Hierarchical Structure in Neural Stacks Using Unsupervised Parsing

Merrill, W., Khazan, L., Amsel, N., Hao, Y., Mendelsohn, S., & Frank, R. (2019) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w19-4823 · Cited by 6

Fine-grained Interpretation and Causation Analysis in Deep NLP Models

Sajjad, H., Kokhlikyan, N., Dalvi, F., & Durrani, N. (2021) · doi:10.18653/v1/2021.naacl-tutorials.2 · Cited by 5

Explainability of neural networks for child language: Agent‐First strategy in comprehension of Korean active transitive construction

Shin, G., & Mun, S. (2023) · Developmental Science · doi:10.1111/desc.13405 · Cited by 7

Untitled work

(2022) · doi:10.18653/v1/2022.conll-1.8 · Cited by 5

Can Language Models Be Tricked by Language Illusions? Easier with Syntax, Harder with Semantics

Zhang, Y., Gibson, E. A. F., & Davis, F. (2023) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2023.conll-1.1 · Cited by 8

Filtered Corpus Training (FiCT) Shows that Language Models Can Generalize from Indirect Evidence

Patil, A., Jumelet, J., Chiu, Y. Y., Lapastora, A., Shen, P. S., Wang, L., Willrich, C., & Steinert‐Threlkeld, S. (2024) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00720 · Cited by 16

Grammaticality and Language Modelling

Niu, J., & Penn, G. (2020) · Workshop on Evaluation and Comparison of NLP Systems (Eval4NLP) · doi:10.18653/v1/2020.eval4nlp-1.11 · Cited by 6

Explicitly Modeling Syntax in Language Models with Incremental Parsing and a Dynamic Oracle

Shen, Y., Tan, S., Sordoni, A., Reddy, S., & Courville, A. C. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.132 · Cited by 8

Assessing the Generalization Capacity of Pre-trained Language Models through Japanese Adversarial Natural Language Inference

Yanaka, H., & Mineshima, K. (2021) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2021.blackboxnlp-1.26 · Cited by 7

Assessing the Benchmarking Capacity of Machine Reading Comprehension DatasetsPreprint

Sugawara, S., Stenetorp, P., Inui, K., & Aizawa, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.09241 · Cited by 5

How Context Affects Language Models' Factual Predictions

Fabio Petroni, P. L. (2020) · Automated Knowledge Base Construction (AKBC) · doi:10.24432/c5201w · Cited by 4

Assisted Process Knowledge Graph Building Using Pre-trained Language Models

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Shades of zero: Distinguishing impossibility from inconceivability

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Multilingual capabilities of GPT: A study of structural ambiguity

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An LSTM Adaptation Study of (Un)grammaticality

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Cross-Linguistic Syntactic Evaluation of Word Prediction Models

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Refining Targeted Syntactic Evaluation of Language Models

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Language Modeling with Syntactic and Semantic Representation for Sentence Acceptability Predictions

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