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Lingpeng Kong
Lingpeng Kong
Google DeepMind, The University of Hong Kong
Verified email at cs.hku.hk - Homepage
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
2862014
Dynet: The dynamic neural network toolkit
G Neubig, C Dyer, Y Goldberg, A Matthews, W Ammar, A Anastasopoulos, ...
arXiv preprint arXiv:1701.03980, 2017
2632017
Random feature attention
H Peng, N Pappas, D Yogatama, R Schwartz, NA Smith, L Kong
arXiv preprint arXiv:2103.02143, 2021
2302021
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
1512019
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
1442016
Segmental recurrent neural networks
L Kong, C Dyer, NA Smith
arXiv preprint arXiv:1511.06018, 2015
1292015
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
1062022
Diffuseq: Sequence to sequence text generation with diffusion models
S Gong, M Li, J Feng, Z Wu, LP Kong
arXiv preprint arXiv:2210.08933, 2022
1002022
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
992022
A contrastive framework for neural text generation
Y Su, T Lan, Y Wang, D Yogatama, L Kong, N Collier
Advances in Neural Information Processing Systems 35, 21548-21561, 2022
812022
Adaptive semiparametric language models
D Yogatama, C de Masson d’Autume, L Kong
Transactions of the Association for Computational Linguistics 9, 362-373, 2021
802021
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
732016
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
632019
Zerogen: Efficient zero-shot learning via dataset generation
J Ye, J Gao, Q Li, H Xu, J Feng, Z Wu, T Yu, L Kong
arXiv preprint arXiv:2202.07922, 2022
512022
Cyprien de Masson d’Autume, Lei Yu, Wang Ling, Zihang Dai, and Dani Yogatama. 2020. A mutual information maximization perspective of language representation learning
L Kong
8th International Conference on Learning Representations, ICLR, 26-30, 2020
422020
Document context language models
Y Ji, T Cohn, L Kong, C Dyer, J Eisenstein
arXiv preprint arXiv:1511.03962, 2015
412015
Bayesian Optimization of Text Representations
D Yogatama, L Kong, NA Smith
Proceedings of the Conference on Empirical Methods in Natural Language …, 2015
412015
Language models can see: Plugging visual controls in text generation
Y Su, T Lan, Y Liu, F Liu, D Yogatama, Y Wang, L Kong, N Collier
arXiv preprint arXiv:2205.02655, 2022
402022
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
372020
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