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Lingpeng Kong
Lingpeng Kong
Google DeepMind, The University of Hong Kong
Zweryfikowany adres z cs.hku.hk - Strona główna
Tytuł
Cytowane przez
Cytowane przez
Rok
A Dependency Parser for Tweets
L Kong, N Schneider, S Swayamdipta, A Bhatia, C Dyer, NA Smith
EMNLP 2014, 2014
2732014
Dynet: The dynamic neural network toolkit
G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ...
arXiv preprint arXiv:1701.03980, 2017
2462017
Random feature attention
H Peng, N Pappas, D Yogatama, R Schwartz, NA Smith, L Kong
arXiv preprint arXiv:2103.02143, 2021
1432021
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
1332016
Segmental recurrent neural networks
L Kong, C Dyer, NA Smith
arXiv preprint arXiv:1511.06018, 2015
1112015
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
1042019
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
802016
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
642016
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
482019
Adaptive semiparametric language models
D Yogatama, C de Masson d’Autume, L Kong
Transactions of the Association for Computational Linguistics 9, 362-373, 2021
452021
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
392019
Document context language models
Y Ji, T Cohn, L Kong, C Dyer, J Eisenstein
arXiv preprint arXiv:1511.03962, 2015
362015
Bayesian Optimization of Text Representations
D Yogatama, L Kong, NA Smith
Proceedings of the Conference on Empirical Methods in Natural Language …, 2015
362015
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
352017
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
332017
Unifiedskg: Unifying and multi-tasking structured knowledge grounding with text-to-text language models
T Xie, CH Wu, P Shi, R Zhong, T Scholak, M Yasunaga, CS Wu, M Zhong, ...
arXiv preprint arXiv:2201.05966, 2022
322022
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
322017
Transforming Dependencies into Phrase Structures
L Kong, AM Rush, NA Smith
NAACL-HLT, 2015
322015
cosFormer: Rethinking Softmax in Attention
Z Qin, W Sun, H Deng, D Li, Y Wei, B Lv, J Yan, L Kong, Y Zhong
arXiv preprint arXiv:2202.08791, 2022
302022
An empirical comparison of parsing methods for stanford dependencies
L Kong, NA Smith
arXiv preprint arXiv:1404.4314, 2014
292014
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