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# Who cites «Transition-Based Dependency Parsing with Stack Long Short-Term Memory» · page 3

304 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/missing-2015-dyer-transition-based-dependency-parsing-stack/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/missing-2015-dyer-transition-based-dependency-parsing-stack/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/missing-2015-dyer-transition-based-dependency-parsing-stack-2/) · **3** · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/missing-2015-dyer-transition-based-dependency-parsing-stack-4/)

1. [Greedy, Joint Syntactic-Semantic Parsing with Stack LSTMs](https://aclanthology.org/K16-1019/) *— 2016 · Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning*
2. [Incremental Parsing with Minimal Features Using Bi-Directional LSTM](https://aclanthology.org/P16-2006/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
3. [Keystroke dynamics as signal for shallow syntactic parsing](https://aclanthology.org/C16-1059/) *— 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers*
4. [LSTM CCG Parsing](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2016-lewis-lstm-ccg-parsing/) *— our card · 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
5. [LSTM Shift-Reduce CCG Parsing](https://aclanthology.org/D16-1181/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
6. [Learning Continuous Semantic Representations of Symbolic Expressions](https://arxiv.org/abs/1611.01423) *— 2016 · arXiv:1611.01423*
7. [Learning Crosslingual Word Embeddings without Bilingual Corpora](https://aclanthology.org/D16-1136/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
8. [Learning Distributed Word Representations For Bidirectional LSTM Recurrent Neural Network](https://aclanthology.org/N16-1064/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
9. [Learning Global Features for Coreference Resolution](https://aclanthology.org/N16-1114/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
10. [Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition](https://aclanthology.org/W16-5102/) *— 2016 · Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016)*
11. [Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning](https://aclanthology.org/P16-1013/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [Long Short-Term Memory-Networks for Machine Reading](https://aclanthology.org/D16-1053/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
13. [Many Languages, One Parser](https://aclanthology.org/Q16-1031/) *— 2016 · Transactions of the Association for Computational Linguistics*
14. [Mapping Unseen Words to Task-Trained Embedding Spaces](https://aclanthology.org/W16-1612/) *— 2016 · Proceedings of the 1st Workshop on Representation Learning for NLP*
15. [Massively Multilingual Word Embeddings](https://arxiv.org/abs/1602.01925) *— 2016 · arXiv:1602.01925*
16. [Measuring the Information Content of Financial News](https://aclanthology.org/C16-1303/) *— 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers*
17. [Neural Architectures for Named Entity Recognition](https://aclanthology.org/N16-1030/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
18. [Neural Network-Based Model for Japanese Predicate Argument Structure Analysis](https://aclanthology.org/P16-1117/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [Neural Shift-Reduce CCG Semantic Parsing](https://aclanthology.org/D16-1183/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
20. [Optimizing Performance of Recurrent Neural Networks on GPUs](https://arxiv.org/abs/1604.01946) *— 2016 · arXiv:1604.01946*
21. [Polyglot Neural Language Models: A Case Study in Cross-Lingual Phonetic Representation Learning](https://aclanthology.org/N16-1161/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
22. [Probabilistic Graph-based Dependency Parsing with Convolutional Neural Network](https://aclanthology.org/P16-1131/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [Prédiction structurée pour l''analyse syntaxique en constituants par transitions : modèles denses et modèles creux Structured Prediction for Transition-based Constituent Parsing: Dense and Sparse Models](https://aclanthology.org/2016.tal-1.3/) *— 2016*
24. [Recurrent Dropout without Memory Loss](https://aclanthology.org/C16-1165/) *— 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers*
25. [Recurrent Memory Networks for Language Modeling](https://aclanthology.org/N16-1036/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
26. [Recurrent Neural Network Grammars](https://aclanthology.org/N16-1024/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
27. [Shift-Reduce CCG Parsing using Neural Network Models](https://aclanthology.org/N16-1052/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
28. [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://md.mova.elamur.ai/free/en/docs/papers/narrative-2016-kiperwasser-simple-accurate-dependency-parsing-bidirectional/) *— our card · 2016*
29. [Span-Based Constituency Parsing with a Structure-Label System and Provably Optimal Dynamic Oracles](https://arxiv.org/abs/1612.06475) *— 2016 · arXiv:1612.06475*
30. [Stack-propagation: Improved Representation Learning for Syntax](https://aclanthology.org/P16-1147/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
31. [The SIGMORPHON 2016 Shared Task–-Morphological Reinflection](https://aclanthology.org/W16-2002/) *— 2016 · Proceedings of the 14th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology*
32. [Top-down Tree Long Short-Term Memory Networks](https://aclanthology.org/N16-1035/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
33. [Training with Exploration Improves a Greedy Stack LSTM Parser](https://aclanthology.org/D16-1211/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
34. [Transition-Based Dependency Parsing with Heuristic Backtracking](https://aclanthology.org/D16-1254/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
35. [Transition-Based Neural Word Segmentation](https://aclanthology.org/P16-1040/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
36. [Transition-based dependency parsing with topological fields](https://aclanthology.org/P16-2001/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
37. [Weighting Finite-State Transductions With Neural Context](https://aclanthology.org/N16-1076/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
38. [Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM](https://aclanthology.org/P16-1150/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [A Bayesian Model for Generative Transition-based Dependency Parsing](https://aclanthology.org/W15-2108/) *— 2015 · Proceedings of the Third International Conference on Dependency Linguistics (Depling 2015)*
40. [A Neural Probabilistic Structured-Prediction Model for Transition-Based Dependency Parsing](https://aclanthology.org/P15-1117/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
41. [A Primer on Neural Network Models for Natural Language Processing](https://arxiv.org/abs/1510.00726) *— 2015 · arXiv:1510.00726*
42. [An Improved Non-monotonic Transition System for Dependency Parsing](https://md.mova.elamur.ai/free/en/docs/papers/missing-2015-honnibal-improved-non-monotonic-transition-system/) *— our card · 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
43. [Generative Incremental Dependency Parsing with Neural Networks](https://aclanthology.org/P15-2142/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
44. [Improved Transition-Based Parsing and Tagging with Neural Networks](https://aclanthology.org/D15-1159/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
45. [Improved Transition-based Parsing by Modeling Characters instead of Words with LSTMs](https://aclanthology.org/D15-1041/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
46. [Learning to Transduce with Unbounded Memory](https://arxiv.org/abs/1506.02516) *— 2015 · arXiv:1506.02516*
47. [Semi-supervised Dependency Parsing using Bilexical Contextual Features from Auto-Parsed Data](https://aclanthology.org/D15-1158/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
48. [Structured Training for Neural Network Transition-Based Parsing](https://aclanthology.org/P15-1032/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
49. [The Goldilocks Principle: Reading Children''s Books with Explicit Memory Representations](https://arxiv.org/abs/1511.02301) *— 2015 · arXiv:1511.02301*
50. [Transition-based Spinal Parsing](https://aclanthology.org/K15-1029/) *— 2015 · Proceedings of the Nineteenth Conference on Computational Natural Language Learning*
51. [Tree-structured composition in neural networks without tree-structured architectures](https://arxiv.org/abs/1506.04834) *— 2015 · arXiv:1506.04834*
52. [A Credit Assignment Compiler for Joint Prediction](https://arxiv.org/abs/1406.1837) *— 2014 · arXiv:1406.1837*
53. antonelli-2018-parsing-italian-texts-together-is
54. aufrant-2018-exploiting-dynamic-oracles-train-projective
55. ballesteros-2017-arc-standard-spinal-parsing-stack
56. bauer-2025-high-accuracy-transition-based-constituency
57. chen-2015-transition-based-dependency-parsing-two
58. chitkara-2019-topic-spotting-hierarchical-networks-self
59. doan-2020-joint-deep-contextualized-word-representation
60. espinosa-2016-learning-recognise-named-entities-tweets
61. fegade-2023-acrobat-optimizing-auto-batching-dynamic
62. floquet-2025-scaling-graph-based-dependency-parsing
63. fu-2018-hccl-semeval-2018-task-8
64. gao-2020-rp-dnn-tweet-level-propagation
65. hale-2019-text-genre-training-data-size
66. henderson-2023-transformers-as-graph-graph-models
67. heyman-2019-learning-unsupervised-multilingual-word-embeddings
68. hu-2022-scene-graph-modification-as-incremental
69. jansson-2017-distributed-representation-lda-topic-modelling
70. ji-2021-neural-transition-based-joint-model
71. ji-2021-unified-encoding-structures-transition-systems
72. kohita-2018-dynamic-feature-selection-attention-incremental
73. komatsu-2015-reducing-lexical-features-parsing-word
74. lhoneux-2020-what-should-do-can-lstms
75. libovicky-2016-neural-scoring-function-mst-parser
76. lu-2018-m-cner-corpus-chinese-named
77. magnini-2020-comparing-machine-learning-deep-learning
78. mitra-2020-deeply-embedded-knowledge-representation-reasoning
79. morio-2022-hitachi-semeval-2022-task-10
80. nguyen-2018-treatment-side-effect-prediction-online
81. noji-2023-how-much-syntactic-supervision-is
82. ramanarayanan-2018-automatic-token-turn-level-language
83. ravi-2021-soda-device-conversational-slot-extraction
84. ren-2018-building-ellipsis-aware-chinese-dependency
85. ruano-2025-effective-multi-task-learning-biomedical
86. shen-2016-making-dependency-labeling-simple-fast
87. singh-2018-pushing-limits-radiology-joint-modeling
88. stahlberg-2018-operation-sequence-model-explainable-neural
89. stanojevic-2017-neural-discontinuous-constituency-parsing
90. stanojevic-2019-ccg-parsing-algorithm-incremental-tree
91. stanojevic-2020-max-margin-incremental-ccg-parsing
92. steingrimsson-2020-facilitating-corpus-usage-making-icelandic
93. suzuki-2016-right-truncatable-neural-word-embeddings
94. tagami-2023-recurrent-neural-network-ccg-parser
95. takase-2016-composing-distributed-representations-relational-patterns
96. tran-2018-multiplicative-tree-structured-long-short
97. xu-2017-tag-parser-evaluation-textual-entailments
98. xu-2022-multi-layer-pseudo-siamese-biaffine
99. yoshida-2025-investigating-psychometric-predictive-power-syntactic
100. zhang-2015-combining-discrete-continuous-features-deterministic

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