Designing and Interpreting Probes with Control Tasks

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071

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Distributed Representations of Words and Phrases and their Compositionality

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Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983

Building a Large Annotated Corpus of English: The Penn Treebank

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Understanding deep learning requires rethinking generalization

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Generating Typed Dependency Parses from Phrase Structure Parses

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Rademacher and Gaussian Complexities: Risk Bounds and Structural Results

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Linguistic Knowledge and Transferability of Contextual Representations

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What do you learn from context? Probing for sentence structure in contextualized word representations

Tenney, I., Xia, P., Chen, B., Wang, A., Poliak, A., McCoy, R. T., Kim, N., Van Durme, B., Bowman, S. R., Das, D., et al. (2019) · arXiv · doi:10.48550/arxiv.1905.06316 · Cited by 698

What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties

Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1198 · Cited by 657

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Understanding intermediate layers using linear classifier probes

Alain, G., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1610.01644 · Cited by 581

Analysis Methods in Neural Language Processing: A Survey

Belinkov, Y., & Glass, J. R. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00254 · Cited by 522

Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction TasksPreprint

Adi, Y., Kermany, E., Belinkov, Y., Lavi, O., & Goldberg, Y. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.04207 · Cited by 446

Dissecting Contextual Word Embeddings: Architecture and Representation

Peters, M., Neumann, M., Zettlemoyer, L., & Yih, W.-T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1179 · Cited by 434

What you can cram into a single vector: Probing sentence embeddings for linguistic propertiesPreprint

Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.01070 · Cited by 426

Does String-Based Neural MT Learn Source Syntax?

Shi, X., Padhi, I., & Knight, K. K. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1159 · Cited by 328

What do Neural Machine Translation Models Learn about Morphology?

Belinkov, Y., Durrani, N., Dalvi, F., Sajjad, H., & Glass, J. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1080 · Cited by 307

Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction TasksPreprint

Adi, Y. (2024) · arXiv (Cornell University) · doi:10.57702/tcr06vve · Cited by 228

Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging TasksPreprint

Belinkov, Y., Màrquez, L., Sajjad, H., Durrani, N., Dalvi, F., & Glass, J. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.07772 · Cited by 155

Probing for semantic evidence of composition by means of simple classification tasks

Ettinger, A., Elgohary, A., & Resnik, P. (2016) · Workshop on Evaluating Vector-Space Representations for NLP · doi:10.18653/v1/w16-2524 · Cited by 131

Probing What Different NLP Tasks Teach Machines about Function Word Comprehension

Kim, N., Patel, R., Poliak, A., Xia, P., Wang, A., McCoy, T., Tenney, I., Ross, A., Linzen, T., Durme, B. V., et al. (2019) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s19-1026 · Cited by 92

Distributional vectors encode referential attributes

Gupta, A., Boleda, G., Baroni, M., & Padó, S. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1002 · Cited by 79

Analyzing Hidden Representations in End-to-End Automatic Speech Recognition SystemsPreprint

Belinkov, Y., & Glass, J. R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.04482 · Cited by 68

Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information

Giulianelli, M., Harding, J., Mohnert, F., Hupkes, D., & Zuidema, W. H. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5426 · Cited by 57

Assessing Composition in Sentence Vector RepresentationsPreprint

Ettinger, A., Elgohary, A., Phillips, C., & Resnik, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.03992 · Cited by 46

Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task AnalysisPreprint

Zhang, K. W., & Bowman, S. R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.10040 · Cited by 45

Investigating Language Universal and Specific Properties in Word Embeddings

Qian, P., Qiu, X., & Huang, X. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1140 · Cited by 42

LINSPECTOR: Multilingual Probing Tasks for Word Representations

Şahin, G. G., Vania, C., Kuznetsov, I., & Gurevych, I. (2020) · Computational Linguistics · doi:10.1162/coli_a_00376 · Cited by 37

Understanding Learning Dynamics Of Language Models with SVCCA

Saphra, N., & Lopez, A. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1329 · Cited by 31

Arc-swift: A Novel Transition System for Dependency Parsing

Qi, P., & Manning, C. D. (2017) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p17-2018 · Cited by 25

The Lazy Encoder: A Fine-Grained Analysis of the Role of Morphology in Neural Machine Translation

Bisazza, A., & Tump, C. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1313 · Cited by 22

Analyzing Learned Representations of a Deep ASR Performance Prediction Model

Elloumi, Z., Besacier, L., Galibert, O., & Lecouteux, B. (2018) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w18-5402 · Cited by 21

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

Shin, T., Razeghi, Y., Logan IV, R. L., Wallace, E., & Singh, S. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.346 · Cited by 1,279

Explainability for Large Language Models: A Survey

Zhao, H., Chen, H., Yang, F., Liu, N., Deng, H., Cai, H., Wang, S., Yin, D., & Du, M. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3639372 · Cited by 681

FNet: Mixing Tokens with Fourier Transforms

Lee-Thorp, J., Ainslie, J., Eckstein, I., & Ontañón, S. (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.319 · Cited by 614

Factual Probing Is [MASK]: Learning vs. Learning to Recall

Zhong, Z., Friedman, D., & Chen, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.398 · Cited by 274

Improving Syntactic Probing Correctness and Robustness with Control Tasks

Ma, W., Wang, B. Z., Zhang, H., Wang, L., Coto‐Solano, R., Hassanpour, S., & Vosoughi, S. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-short.35

Embodied Knowledge in Natural Language Processing: A Prolog-Based Approach to Selected bAbI Tasks

Cuartero-Sánchez, J. M. (2025) · doi:10.13140/rg.2.2.29837.65763

A study on Inference Control in Natural Language Processing(自然言語処理における推論の制御に関する研究)

宮田, 高. (1996) · doi:10.11501/3139771

The integration of language and content: action research on a theory of task design

Jones, A., & Sin, S. (2007) · Journal of Applied Linguistics · doi:10.1558/japl.v1i1.95 · Cited by 2

Natural Language Interface Put in Perspective: Interaction of Search Method and Task Complexity

Wang, Q., Hu, J., & Nass, C. (2005) · Natural Language Processing and Cognitive Science · doi:10.5220/0002566000030012