---
title: "Who cites Deep Biaffine Attention for Neural Dependency…"
description: "The papers of the Elamur Research library that cite «Deep Biaffine Attention for Neural Dependency Parsing», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/missing-2016-dozat-deep-biaffine-attention-neural-dependency/cited-by/page/7/"
---
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# Who cites «Deep Biaffine Attention for Neural Dependency Parsing» · page 7

627 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/missing-2016-dozat-deep-biaffine-attention-neural-dependency/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/missing-2016-dozat-deep-biaffine-attention-neural-dependency/cited-by/) · … · [6](https://md.research.elamur.ai/free/en/papers/missing-2016-dozat-deep-biaffine-attention-neural-dependency/cited-by/page/6/) · **7**

1. [Twitter Universal Dependency Parsing for African-American and Mainstream American English](https://md.research.elamur.ai/free/en/papers/ud-2018-blodgett-twitter-universal-dependency-parsing-african/) *— our card · 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [Two Local Models for Neural Constituent Parsing](https://aclanthology.org/C18-1011/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics*
3. [UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task](https://md.research.elamur.ai/free/en/papers/ud-2018-straka-udpipe-2-0-prototype-conll/) *— our card · 2018 · Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies*
4. [Universal Language Model Fine-tuning for Text Classification](https://aclanthology.org/P18-1031/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [Valency-Augmented Dependency Parsing](https://aclanthology.org/D18-1159/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
6. [What can we learn from Semantic Tagging?](https://aclanthology.org/D18-1526/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
7. [A Minimal Span-Based Neural Constituency Parser](https://aclanthology.org/P17-1076/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing](https://aclanthology.org/K17-3014/) *— 2017 · Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies*
9. [Combining Global Models for Parsing Universal Dependencies](https://aclanthology.org/K17-3003/) *— 2017 · Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies*
10. [Deep Multitask Learning for Semantic Dependency Parsing](https://aclanthology.org/P17-1186/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [Dependency Parsing with Dilated Iterated Graph CNNs](https://aclanthology.org/W17-4301/) *— 2017 · Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing*
12. [Efficient Discontinuous Phrase-Structure Parsing via the Generalized Maximum Spanning Arborescence](https://aclanthology.org/D17-1172/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
13. [Encoder-Decoder Shift-Reduce Syntactic Parsing](https://aclanthology.org/W17-6315/) *— 2017 · Proceedings of the 15th International Conference on Parsing Technologies*
14. [End-to-End Neural Relation Extraction with Global Optimization](https://aclanthology.org/D17-1182/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
15. [Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set](https://aclanthology.org/D17-1002/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
16. [Implicit Syntactic Features for Target-dependent Sentiment Analysis](https://aclanthology.org/I17-1052/) *— 2017 · Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
17. [In-Order Transition-based Constituent Parsing](https://aclanthology.org/Q17-1029/) *— 2017 · Transactions of the Association for Computational Linguistics*
18. [Incremental Graph-based Neural Dependency Parsing](https://aclanthology.org/D17-1173/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
19. [Neural Machine Translation with Source-Side Latent Graph Parsing](https://aclanthology.org/D17-1012/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
20. [Neural Probabilistic Model for Non-projective MST Parsing](https://aclanthology.org/I17-1007/) *— 2017 · Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
21. [Stack-based Multi-layer Attention for Transition-based Dependency Parsing](https://aclanthology.org/D17-1175/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
22. [TAG Parser Evaluation using Textual Entailments](https://aclanthology.org/W17-6214/) *— 2017 · Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms*
23. [TAG Parsing with Neural Networks and Vector Representations of Supertags](https://aclanthology.org/D17-1180/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
24. [Universal Dependencies Parsing for Colloquial Singaporean English](https://md.research.elamur.ai/free/en/papers/ud-2017-wang-universal-dependencies-parsing-colloquial-singaporean/) *— our card · 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks](https://arxiv.org/abs/1611.01587) *— 2016 · arXiv:1611.01587*
26. [What Do Recurrent Neural Network Grammars Learn About Syntax?](https://arxiv.org/abs/1611.05774) *— 2016 · arXiv:1611.05774*
27. [Improved Document Modelling with a Neural Discourse Parser](https://aclanthology.org/U19-1010/) *— 0 · Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association*

Pages: [1](https://md.research.elamur.ai/free/en/papers/missing-2016-dozat-deep-biaffine-attention-neural-dependency/cited-by/) · … · [6](https://md.research.elamur.ai/free/en/papers/missing-2016-dozat-deep-biaffine-attention-neural-dependency/cited-by/page/6/) · **7**