Lingpeng Kong
Lingpeng Kong
Google DeepMind
Verified email at google.com - Homepage
TitleCited byYear
A Dependency Parser for Tweets
L Kong, N Schneider, S Swayamdipta, A Bhatia, C Dyer, NA Smith
EMNLP 2014, 2014
1682014
Dynet: The dynamic neural network toolkit
G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ...
arXiv preprint arXiv:1701.03980, 2017
1342017
What do recurrent neural network grammars learn about syntax?
A Kuncoro, M Ballesteros, L Kong, C Dyer, G Neubig, NA Smith
arXiv preprint arXiv:1611.05774, 2016
602016
Segmental recurrent neural networks
L Kong, C Dyer, NA Smith
arXiv preprint arXiv:1511.06018, 2015
602015
Segmental recurrent neural networks for end-to-end speech recognition
L Lu, L Kong, C Dyer, NA Smith, S Renals
arXiv preprint arXiv:1603.00223, 2016
522016
Distilling an ensemble of greedy dependency parsers into one mst parser
A Kuncoro, M Ballesteros, L Kong, C Dyer, NA Smith
arXiv preprint arXiv:1609.07561, 2016
352016
An empirical comparison of parsing methods for stanford dependencies
L Kong, NA Smith
arXiv preprint arXiv:1404.4314, 2014
252014
Bayesian Optimization of Text Representations
D Yogatama, L Kong, NA Smith
Proceedings of the Conference on Empirical Methods in Natural Language …, 2015
232015
Document context language models
Y Ji, T Cohn, L Kong, C Dyer, J Eisenstein
arXiv preprint arXiv:1511.03962, 2015
222015
SyntaxNet models for the CoNLL 2017 shared task
C Alberti, D Andor, I Bogatyy, M Collins, D Gillick, L Kong, T Koo, J Ma, ...
arXiv preprint arXiv:1703.04929, 2017
212017
Dragnn: A transition-based framework for dynamically connected neural networks
L Kong, C Alberti, D Andor, I Bogatyy, D Weiss
arXiv preprint arXiv:1703.04474, 2017
172017
Transforming Dependencies into Phrase Structures
L Kong, AM Rush, NA Smith
NAACL-HLT, 2015
162015
End-to-end neural segmental models for speech recognition
H Tang, L Lu, L Kong, K Gimpel, K Livescu, C Dyer, NA Smith, S Renals
IEEE Journal of Selected Topics in Signal Processing 11 (8), 1254-1264, 2017
152017
Multitask Learning with CTC and Segmental CRF for Speech Recognition
L Lu, L Kong, C Dyer, NA Smith
arXiv preprint arXiv:1702.06378, 2017
152017
Dependency Parsing for Weibo: An Efficient Probabilistic Logic Programming Approach
WY Wang, L Kong, K Mazaitis, WW Cohen
52014
Learning and evaluating general linguistic intelligence
D Yogatama, CM d'Autume, J Connor, T Kocisky, M Chrzanowski, L Kong, ...
arXiv preprint arXiv:1901.11373, 2019
42019
Episodic Memory in Lifelong Language Learning
CM d'Autume, S Ruder, L Kong, D Yogatama
arXiv preprint arXiv:1906.01076, 2019
22019
Acbima: Advanced chinese bi-character word morphological analyzer
TH Huang, YN Chen, L Kong
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing, 26-31, 2015
22015
Improving Chinese Dependency Parsing with Self-Disambiguating Patterns
L Qiu, L Wu, K Zhao, C Hu, L Kong
2011 International Conference on Asian Language Processing, 7-10, 2011
22011
Formalization and Rules for Recognition of Satirical Irony
L Kong, L Qiu
2011 International Conference on Asian Language Processing, 135-138, 2011
22011
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Articles 1–20