Who cites «Deep Biaffine Attention for Neural Dependency Parsing» · page 7

626 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

Two Local Models for Neural Constituent Parsing — 2018 · Proceedings of the 27th International Conference on Computational Linguistics
UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task — our card · 2018 · Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Universal Language Model Fine-tuning for Text Classification — 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Valency-Augmented Dependency Parsing — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
What can we learn from Semantic Tagging? — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
A Minimal Span-Based Neural Constituency Parser — 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing — 2017 · Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Combining Global Models for Parsing Universal Dependencies — 2017 · Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
Deep Multitask Learning for Semantic Dependency Parsing — 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Dependency Parsing with Dilated Iterated Graph CNNs — 2017 · Proceedings of the 2nd Workshop on Structured Prediction for Natural Language Processing
Efficient Discontinuous Phrase-Structure Parsing via the Generalized Maximum Spanning Arborescence — 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Encoder-Decoder Shift-Reduce Syntactic Parsing — 2017 · Proceedings of the 15th International Conference on Parsing Technologies
End-to-End Neural Relation Extraction with Global Optimization — 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Fast(er) Exact Decoding and Global Training for Transition-Based Dependency Parsing via a Minimal Feature Set — 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Implicit Syntactic Features for Target-dependent Sentiment Analysis — 2017 · Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
In-Order Transition-based Constituent Parsing — 2017 · Transactions of the Association for Computational Linguistics
Incremental Graph-based Neural Dependency Parsing — 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Neural Machine Translation with Source-Side Latent Graph Parsing — 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Neural Probabilistic Model for Non-projective MST Parsing — 2017 · Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Stack-based Multi-layer Attention for Transition-based Dependency Parsing — 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
TAG Parser Evaluation using Textual Entailments — 2017 · Proceedings of the 13th International Workshop on Tree Adjoining Grammars and Related Formalisms
TAG Parsing with Neural Networks and Vector Representations of Supertags — 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Universal Dependencies Parsing for Colloquial Singaporean English — our card · 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks — 2016 · arXiv:1611.01587
What Do Recurrent Neural Network Grammars Learn About Syntax? — 2016 · arXiv:1611.05774
Improved Document Modelling with a Neural Discourse Parser — 0 · Proceedings of the 17th Annual Workshop of the Australasian Language Technology Association