---
title: "Who cites Identifying Cascading Errors using Constraints…"
description: "The papers of the Elamur Research library that cite «Identifying Cascading Errors using Constraints in Dependency Parsing», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/aclbulk-2015-ng-identifying-cascading-errors-constraints-dependency/cited-by/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Identifying Cascading Errors using Constraints in…](https://md.research.elamur.ai/free/en/papers/aclbulk-2015-ng-identifying-cascading-errors-constraints-dependency/) / cited by

# Who cites «Identifying Cascading Errors using Constraints in Dependency Parsing»

4 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/aclbulk-2015-ng-identifying-cascading-errors-constraints-dependency/)

1. [Evaluating Universal Dependency Parser Recovery of Predicate Argument Structure via CompChain Analysis](https://md.research.elamur.ai/free/en/papers/ud-2021-indurkhya-evaluating-universal-dependency-parser-recovery/) *— our card · 2021 · Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics*
2. [Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples](https://md.research.elamur.ai/free/en/papers/zheng-2020-evaluating-enhancing-robustness-neural-network/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
3. [Valency-Augmented Dependency Parsing](https://aclanthology.org/D18-1159/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
4. [Tackling Error Propagation through Reinforcement Learning: A Case of Greedy Dependency Parsing](https://aclanthology.org/E17-1064/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers*
