Studying the Inductive Biases of RNNs with Synthetic Variations of Natural Languages
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Shauli Ravfogel, Yoav Goldberg, Tal Linzen. 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). 2019.
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In basic syntactic reasoning, retaining only sentences whose SVO variant is identical to the original English sentence eliminates the complexity of unnatural SVO sentences.
“To eliminate this complexity, we retain only sentences whose SVO variant according to Ravfogel et al. (2019)'s data generation procedure is identical to the original English sentence.”
From Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks (2024) · cites this paper as Ravfogel et al. (2019)
The passages available from our copy are bibliographic metadata, not the paper’s methods or results. Searches for “SVO,” the data-generation procedure, and identical English sentences returned no passage describing the procedure. The citing authors’ decision to retain sentences is their own; this copy cannot verify the procedure they attribute to Ravfogel et al. (LLM, conf=0.72)
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Targeted Syntactic Evaluation of Language Models
Marvin, R., & Linzen, T. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1151 · Cited by 334 · see citing works
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McCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00567 · Cited by 70 · see citing works
Self-citationMcCoy, R. T., Smolensky, P., Linzen, T., Gao, J., & Celikyilmaz, A. (2021) · arXiv · doi:10.48550/arxiv.2111.09509 · Cited by 70 · see citing works
Self-citationWu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., & Kim, Y. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.102 · Cited by 58 · see citing works
- Authors
- Ravfogel, S., Goldberg, Y., & Linzen, T.
- Venue
- 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)
- Published
- 2019
- Pages
- 3532-3542
- OpenAlex
- W2963351454
- Cited by
- 39
- References
- 1
- Citations by year
- 2023: 8 · 2022: 5 · 2021: 9 · 2020: 12 · 2019: 5
- FWCI
- 3.06
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- recurrent neural networks, inductive bias, language variation, language modeling, synthetic data
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