---
title: "Who cites Transition-Based Dependency Parsing with Stack…"
description: "304 papers of the Elamur Research library cite «Transition-Based Dependency Parsing with Stack Long Short-Term Memory», found in their bibliographies…"
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---
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# Who cites «Transition-Based Dependency Parsing with Stack Long Short-Term Memory»

304 papers of our library cite it (of the 140,884 whose bibliographies we read) · 45 with a page here, the most cited first · [← the paper](https://md.research.elamur.ai/free/en/papers/dyer-2015-transition-based-dependency-parsing-stack/)

1. [**Relational inductive biases, deep learning, and graph networks**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,285 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
2. [**Deep Biaffine Attention for Neural Dependency Parsing**Timothy Dozat, Christopher D. Manning*2016 · 1,320 citations*](https://md.research.elamur.ai/free/en/papers/dozat-2016-deep-biaffine-attention-neural-dependency/)
3. [**Long Short-Term Memory-Networks for Machine Reading**Jianpeng Cheng, Li Dong, Mirella Lapata*2016 · 1,300 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2016-long-short-term-memory-networks/)
4. [**Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling**Diego Marcheggiani, Ivan Titov*2017 · 1,101 citations*](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-encoding-sentences-graph-convolutional-networks/)
5. [**Neural Architectures for Named Entity Recognition**Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, et al.*2016 · 1,021 citations*](https://md.research.elamur.ai/free/en/papers/lample-2016-neural-architectures-named-entity-recognition/)
6. [**Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations**Eliyahu Kiperwasser, Yoav Goldberg*2016 · 903 citations*](https://md.research.elamur.ai/free/en/papers/kiperwasser-2016-simple-accurate-dependency-parsing-bidirectional/)
7. [**End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF**Xuezhe Ma, Eduard Hovy*2016 · 852 citations*](https://md.research.elamur.ai/free/en/papers/ma-2016-end-end-sequence-labeling-bi/)
8. [**Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations**Sosuke Kobayashi*2018 · 804 citations*](https://md.research.elamur.ai/free/en/papers/kobayashi-2018-contextual-augmentation-data-augmentation-words/)
9. [**An Improved Non-monotonic Transition System for Dependency Parsing**Matthew Honnibal, Mark Johnson*2015 · 749 citations*](https://md.research.elamur.ai/free/en/papers/honnibal-2015-improved-non-monotonic-transition-system/)
10. [**The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations**Felix Hill, Antoine Bordes, Sumit Chopra, et al.*2015 · 693 citations*](https://md.research.elamur.ai/free/en/papers/hill-2015-goldilocks-principle-reading-children-s/)
11. [**Globally Normalized Transition-Based Neural Networks**Daniel Andor, Chris Alberti, David Weiss, et al.*2016 · 676 citations*](https://md.research.elamur.ai/free/en/papers/andor-2016-globally-normalized-transition-based-neural/)
12. [**A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks**Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, et al.*2016 · 666 citations*](https://md.research.elamur.ai/free/en/papers/hashimoto-2016-joint-many-task-model-growing/)
13. [**DyNet: The Dynamic Neural Network Toolkit**Graham Neubig, Chris Dyer, Yoav Goldberg, et al.*2017 · 485 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2017-dynet-dynamic-neural-network-toolkit/)
14. [**Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs**Miguel Ballesteros, Chris Dyer, Noah A. Smith*2015 · 401 citations*](https://md.research.elamur.ai/free/en/papers/ballesteros-2015-improved-transition-based-parsing-modeling/)
15. [**Massively Multilingual Word Embeddings**Waleed Ammar, George Mulcaire, Yulia Tsvetkov, et al.*2016 · 399 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2016-massively-multilingual-word-embeddings/)
16. [**Many Languages, One Parser**Waleed Ammar, George Mulcaire, Miguel Ballesteros, et al.*2016 · 382 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2016-many-languages-one-parser/)
17. [**UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task**Milan Straka*2018 · 303 citations*](https://md.research.elamur.ai/free/en/papers/straka-2018-udpipe-2-0-prototype-conll/)
18. [**Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM**Ivan Habernal, Iryna Gurevych*2016 · 302 citations*](https://md.research.elamur.ai/free/en/papers/habernal-2016-which-argument-is-more-convincing/)
19. [**Survey: Multiword Expression Processing: A Survey**Mathieu Constant, Gülşen Eryiǧit, Johanna Monti, et al.*2017 · 296 citations*](https://md.research.elamur.ai/free/en/papers/constant-2017-survey-multiword-expression-processing-survey/)
20. [**Simpler but More Accurate Semantic Dependency Parsing**Timothy Dozat, Christopher D. Manning*2018 · 271 citations*](https://md.research.elamur.ai/free/en/papers/dozat-2018-simpler-but-more-accurate-semantic/)
21. [**Learning Global Features for Coreference Resolution**Sam Wiseman, Alexander M. Rush, Stuart M. Shieber*2016 · 264 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2016-learning-global-features-coreference-resolution/)
22. [**Neural End-to-End Learning for Computational Argumentation Mining**Steffen Eger, Johannes Daxenberger, Iryna Gurevych*2017 · 263 citations*](https://md.research.elamur.ai/free/en/papers/eger-2017-neural-end-end-learning-computational/)
23. [**Learning to Parse and Translate Improves Neural Machine Translation**Akiko Eriguchi, Yoshimasa Tsuruoka, Kyunghyun Cho*2017 · 214 citations*](https://md.research.elamur.ai/free/en/papers/eriguchi-2017-learning-parse-translate-improves-neural/)
24. [**Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles**James Cross, Liang Huang*2016 · 191 citations*](https://md.research.elamur.ai/free/en/papers/cross-2016-span-based-constituency-parsing-structure/)
25. [**Stance Detection with Hierarchical Attention Network**Qingying Sun, Zhongqing Wang, Qiaoming Zhu, et al.*2018 · 133 citations*](https://md.research.elamur.ai/free/en/papers/sun-2018-stance-detection-hierarchical-attention-network/)
26. [**LSTM CCG Parsing**Mike Lewis, Kenton Lee, Luke Zettlemoyer*2016 · 120 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2016-lstm-ccg-parsing/)
27. [**A Joint Sequential and Relational Model for Frame-Semantic Parsing**Bishan Yang, Tom Mitchell*2017 · 98 citations*](https://md.research.elamur.ai/free/en/papers/yang-2017-joint-sequential-relational-model-frame/)
28. [**In-Order Transition-based Constituent Parsing**Jiangming Liu, Yue Zhang*2017 · 98 citations*](https://md.research.elamur.ai/free/en/papers/liu-2017-order-transition-based-constituent-parsing/)
29. [**A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling**Diego Marcheggiani, Anton Frolov, Ivan Titov*2017 · 86 citations*](https://md.research.elamur.ai/free/en/papers/marcheggiani-2017-simple-accurate-syntax-agnostic-neural/)
30. [**Universal Dependencies Parsing for Colloquial Singaporean English**Hongmin Wang, Yue Zhang, GuangYong Leonard Chan, et al.*2017 · 49 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-universal-dependencies-parsing-colloquial-singaporean/)
31. [**Learning Contextually Informed Representations for Linear-Time Discourse Parsing**Yang Liu, Mirella Lapata*2017 · 44 citations*](https://md.research.elamur.ai/free/en/papers/liu-2017-learning-contextually-informed-representations-linear/)
32. [**Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from Modern Hebrew**Adam Amram, Anat Ben David, Reut Tsarfaty*2018 · 40 citations*](https://md.research.elamur.ai/free/en/papers/amram-2018-representations-architectures-neural-sentiment-analysis/)
33. [**Neural Generative Rhetorical Structure Parsing**Amandla Mabona, Laura Rimell, Stephen Clark, et al.*2019 · 35 citations*](https://md.research.elamur.ai/free/en/papers/mabona-2019-neural-generative-rhetorical-structure-parsing/)
34. [**Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement**Alireza Mohammadshahi, James Henderson*2021 · 26 citations*](https://md.research.elamur.ai/free/en/papers/mohammadshahi-2021-recursive-non-autoregressive-graph-graph/)
35. [**Graph-to-Graph Transformer for Transition-based Dependency Parsing**Alireza Mohammadshahi, James Henderson*2020 · 23 citations*](https://md.research.elamur.ai/free/en/papers/mohammadshahi-2020-graph-graph-transformer-transition-based/)
36. [**Modeling Speech Acts in Asynchronous Conversations: A Neural-CRF Approach**Shafiq Joty, Tasnim Mohiuddin*2018 · 20 citations*](https://md.research.elamur.ai/free/en/papers/joty-2018-modeling-speech-acts-asynchronous-conversations/)
37. [**ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer**Ebrahim Chekol Jibril, A. Cuneyd Tant\ug*2022 · 18 citations*](https://md.research.elamur.ai/free/en/papers/jibril-2022-anec-amharic-named-entity-corpus/)
38. [**Applying Occam's Razor to Transformer-Based Dependency Parsing: What Works, What Doesn't, and What is Really Necessary**Stefan Grünewald, Annemarie Friedrich, Jonas Kuhn*2021 · 18 citations*](https://md.research.elamur.ai/free/en/papers/grunewald-2021-applying-occam-s-razor-transformer/)
39. [**Nucleus Composition in Transition-based Dependency Parsing**Joakim Nivre, Ali Basirat, Luise Dürlich, et al.*2022 · 9 citations*](https://md.research.elamur.ai/free/en/papers/nivre-2022-nucleus-composition-transition-based-dependency/)
40. [**The DCU-EPFL Enhanced Dependency Parser at the IWPT 2021 Shared Task**James Barry, Alireza Mohammadshahi, Joachim Wagner, et al.*2021 · 4 citations*](https://md.research.elamur.ai/free/en/papers/barry-2021-dcu-epfl-enhanced-dependency-parser/)
41. [**Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs**Hiroshi Matsuda, Chunpeng Ma, Masayuki Asahara*2025 · 2 citations*](https://md.research.elamur.ai/free/en/papers/matsuda-2025-step-step-instructions-simple-tabular/)
42. [**Comparing Neural Network Parsers for a Less-resourced and Morphologically-rich Language: Amharic Dependency Parser**Binyam Ephrem Seyoum, Yusuke Miyao, Baye Yimam Mekonnen*2020 · 2 citations*](https://md.research.elamur.ai/free/en/papers/seyoum-2020-comparing-neural-network-parsers-less/)
43. [**Prepositional Phrase Attachment over Word Embedding Products**Pranava Swaroop Madhyastha, Xavier Carreras, Ariadna Quattoni*2017 · 1 citation*](https://md.research.elamur.ai/free/en/papers/madhyastha-2017-prepositional-phrase-attachment-over-word/)
44. [**Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction**Tianze Shi, Lillian Lee*2021*](https://md.research.elamur.ai/free/en/papers/shi-2021-transition-based-bubble-parsing-improvements/)
45. [**Recursive LSTM Tree Representation for Arc-Standard Transition-Based Dependency Parsing**Mohab Elkaref, Bernd Bohnet*2019*](https://md.research.elamur.ai/free/en/papers/elkaref-2019-recursive-lstm-tree-representation-arc/)

259 more papers of our library cite it; they have no page here yet.

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