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Targeted Syntactic Evaluation of Language Models
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We present a dataset for evaluating the grammaticality of the predictions of a language model.We automatically construct a large number of minimally different pairs of English sentences, each consisting of a grammatical and an ungrammatical sentence.The sentence pairs represent different variations of structure-sensitive phenomena: subject-verb agreement, reflexive anaphora and negative polarity items.We expect a language model to assign a higher probability to the grammatical sentence than the ungrammatical one.In an experiment using this data set, an LSTM language model performed poorly on many of the constructions.Multi-task training with a syntactic objective (CCG supertagging) improved the LSTM's accuracy, but a large gap remained between its performance and the accuracy of human participants recruited online.This suggests that there is considerable room for improvement over LSTMs in capturing syntax in a language model.
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“BLiMP, and other related syntactic benchmarks such as those presented in Marvin and Linzen (2018) and Gauthier et al. (2020), were specifically designed to mimic the types of tests invented by linguists and cognitive scientists to reveal syntactic competence-i.e., they are all based on minimal pair sentences.”
From Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora (2023) · cites this paper as Marvin and Linzen (2018)
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Discovering the Compositional Structure of Vector Representations with Role Learning Networks
Soulos, P., McCoy, T., Linzen, T., & Smolensky, P. (2020) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/2020.blackboxnlp-1.23 · Cited by 38
Self-citationHow much pretraining data do language models need to learn syntax?
Pérez-Mayos, L., Ballesteros, M., & Wanner, L. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.118 · Cited by 26
Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus
Trotta, D., Guarasci, R., Leonardelli, E., & Tonelli, S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.250 · Cited by 28
CLiMP: A Benchmark for Chinese Language Model Evaluation
Xiang, B., Yang, C., Li, Y., Warstadt, A., & Kann, K. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.242 · Cited by 33
On the proper role of linguistically-oriented deep net analysis in linguistic theorizingPreprint
Baroni, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.08694 · Cited by 40
Harnessing the linguistic signal to predict scalar inferences
Schuster, S., Chen, Y., & Degen, J. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.479 · Cited by 27
Structural Guidance for Transformer Language Models
Qian, P., Naseem, T., Lévy, R., & Astudillo, R. F. (2021) · doi:10.18653/v1/2021.acl-long.289 · Cited by 26
Analyzing Encoded Concepts in Transformer Language Models
Sajjad, H., Durrani, N., Dalvi, F., Alam, F., Khan, A., & Xu, J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.225 · Cited by 24
Quantum transfer learning for acceptability judgements
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Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing
Maudslay, R. H., & Cotterell, R. (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.11 · Cited by 26
Do RNNs learn human-like abstract word order preferences?Preprint
Futrell, R., & Lévy, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.01866 · Cited by 22
Studying the Inductive Biases of RNNs with Synthetic Variations of Natural LanguagesPreprint
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Self-citation
Metadata
- Authors
- Marvin, R., & Linzen, T.
- Affiliations
- Johns Hopkins University (US)
- Corresponding author
- Linzen, Tal
- ORCID
- Linzen, Tal
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 1192-1202
- OpenAlex
- W2888922637
- Cited by
- 334
- References
- 46
- Citations by year
- 2026: 10 · 2025: 15 · 2024: 21 · 2023: 51 · 2022: 38 · 2021: 77 · 2020: 69 · 2019: 51 · 2018: 2
- FWCI
- 20.41
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- grammaticality judgment, subject-verb agreement, negative polarity items, LSTM language models, human vs machine performance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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