The DOI matches its Crossref record; 44 of the 46 references it cites were found in a registry or an index.
Targeted Syntactic Evaluation of Language Models
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
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
Self-citationAgreement 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
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
Self-citation
Research works printed but not linked: 2
- Anastasia Giannakidou. 2011. Negative and positive polarity items: Variation, licensing, and composi- tionality. In Semantics: An international hand- book of natural language meaning, pages 1660- 1712. Berlin: Mouton de Gruyter.
- Bingley, U.K.: Emerald.
Cited by
Sorted by citations
Language Models as Knowledge Bases?
Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1250 · Cited by 1,834
What Does BERT Look at? An Analysis of BERT’s Attention
Clark, K., Khandelwal, U., Levy, O., & Manning, C. D. (2019) · BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP) · doi:10.18653/v1/w19-4828 · Cited by 1,953
BERT Rediscovers the Classical NLP Pipeline
Tenney, I., Das, D., & Pavlick, E. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1452 · Cited by 1,307
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
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
McCoy, R. T., Pavlick, E., & Linzen, T. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1334 · Cited by 944
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 ›
Are you an author, or do you see a problem with this page? Request a review
