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LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better

Research article (Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018) · cited 146× · AI/ML
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LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better

Summary

LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better is a scholarly article[1].

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  • LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better's instance of is recorded as scholarly article[2].

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

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  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better. Retrieved May 24, 2026, from https://4ort.xyz/entity/lstms-can-learn-syntax-sensitive-dependencies-well-but-modeling-structure-makes-them-better
MLA “LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/lstms-can-learn-syntax-sensitive-dependencies-well-but-modeling-structure-makes-them-better.
BibTeX @misc{4ortxyz_lstms-can-learn-syntax-sensitive-dependencies-well-but-modeling-structure-makes-them-better_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better}}, year = {2026}, url = {https://4ort.xyz/entity/lstms-can-learn-syntax-sensitive-dependencies-well-but-modeling-structure-makes-them-better}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better — https://4ort.xyz/entity/lstms-can-learn-syntax-sensitive-dependencies-well-but-modeling-structure-makes-them-better (retrieved 2026-05-24)

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