Lingpeng Kong

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Citations	1476	1313
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i10-index	21	21

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Chris DyerDeepMind, Carnegie MellonVerified email at google.com
Noah A. SmithUniversity of Washington; Allen Institute for Artificial IntelligenceVerified email at cs.washington.edu
Dani YogatamaDeepMindVerified email at google.com
Adhiguna KuncoroOxford University and Google DeepMindVerified email at google.com
Lei YuDeepMindVerified email at google.com
Wang LingGoogle DeepmindVerified email at cs.cmu.edu
Graham NeubigAssociate Professor of Computer Science, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Swabha SwayamdiptaAllen Institute of Artificial IntelligenceVerified email at allenai.org
Trevor CohnSchool of Computing and Information Systems, University of MelbourneVerified email at unimelb.edu.au
Yangfeng JiComputer Science, University of VirginiaVerified email at virginia.edu
Nathan SchneiderAssistant Professor, Linguistics and Computer Science • Georgetown UniversityVerified email at georgetown.edu
Liang LuStaff Speech Scientist, Otter.aiVerified email at ttic.edu
Archna BhatiaInstitute for Human and Machine CognitionVerified email at ihmc.us
Kevin DuhJohns Hopkins UniversityVerified email at cs.jhu.edu
Manaal FaruquiResearch Scientist, GoogleVerified email at google.com
Steve RenalsProfessor of Speech Technology, University of EdinburghVerified email at ed.ac.uk
Jacob EisensteinGoogle ResearchVerified email at google.com
Alexander M. RushAssociate Professor, Cornell UniversityVerified email at seas.harvard.edu
likun qiuLudong University, ChinaVerified email at pku.edu.cn
William W. CohenGoogle AIVerified email at google.com

Lingpeng Kong

Google DeepMind, The University of Hong Kong

Verified email at cs.hku.hk - Homepage

Natural Language Processing Machine Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
A Dependency Parser for Tweets L Kong, N Schneider, S Swayamdipta, A Bhatia, C Dyer, NA Smith EMNLP 2014, 2014	264	2014
Dynet: The dynamic neural network toolkit G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ... arXiv preprint arXiv:1701.03980, 2017	238	2017
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	130	2016
Segmental recurrent neural networks L Kong, C Dyer, NA Smith arXiv preprint arXiv:1511.06018, 2015	106	2015
Random feature attention H Peng, N Pappas, D Yogatama, R Schwartz, NA Smith, L Kong arXiv preprint arXiv:2103.02143, 2021	94	2021
Episodic memory in lifelong language learning C de Masson D'Autume, S Ruder, L Kong, D Yogatama Advances in Neural Information Processing Systems 32, 2019	80	2019
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	76	2016
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	61	2016
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	41	2019
Bayesian Optimization of Text Representations D Yogatama, L Kong, NA Smith Proceedings of the Conference on Empirical Methods in Natural Language …, 2015	36	2015
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	33	2017
Document context language models Y Ji, T Cohn, L Kong, C Dyer, J Eisenstein arXiv preprint arXiv:1511.03962, 2015	33	2015
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	32	2017
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	32	2017
A mutual information maximization perspective of language representation learning L Kong, CM d'Autume, W Ling, L Yu, Z Dai, D Yogatama arXiv preprint arXiv:1910.08350, 2019	30	2019
Adaptive semiparametric language models D Yogatama, C de Masson d’Autume, L Kong Transactions of the Association for Computational Linguistics 9, 362-373, 2021	29	2021
Transforming Dependencies into Phrase Structures L Kong, AM Rush, NA Smith NAACL-HLT, 2015	29	2015
An empirical comparison of parsing methods for stanford dependencies L Kong, NA Smith arXiv preprint arXiv:1404.4314, 2014	29	2014
Better document-level machine translation with Bayes’ rule L Yu, L Sartran, W Stokowiec, W Ling, L Kong, P Blunsom, C Dyer Transactions of the Association for Computational Linguistics 8, 346-360, 2020	25	2020
Multitask learning with CTC and segmental CRF for speech recognition L Lu, L Kong, C Dyer, NA Smith arXiv preprint arXiv:1702.06378, 2017	17	2017

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