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Huang, Shujian (9)
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The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021 (2)
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., Boxing (1)
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Simple Search
Hits 1 – 10 of 10
1
Analyzing the Intensity of Complaints on Social Media ...
Fang, Ming
;
Zong, Shi
;
Li, Jing
. - : arXiv, 2022
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2
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
Chen, Jiajun
;
Dai, Xinyu
. - : Underline Science Inc., 2021
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3
Adaptive Nearest Neighbor Machine Translation ...
The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021
;
., Boxing
;
Chen, Jiajun
. - : Underline Science Inc., 2021
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4
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction ...
Li, Yanyang
;
Luo, Yingfeng
;
Lin, Ye
. - : arXiv, 2020
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5
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation ...
Zhu, Wenhao
;
Huang, Shujian
;
Pu, Tong
. - : arXiv, 2020
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6
Rethinking Document-level Neural Machine Translation ...
Sun, Zewei
;
Wang, Mingxuan
;
Zhou, Hao
. - : arXiv, 2020
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7
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation ...
Weng, Rongxiang
;
Yu, Heng
;
Huang, Shujian
;
Cheng, Shanbo
;
Luo, Weihua
. - : arXiv, 2019
Abstract:
Pre-training and fine-tuning have achieved great success in the natural language process field. The standard paradigm of exploiting them includes two steps: first, pre-training a model, e.g. BERT, with a large scale unlabeled monolingual data. Then, fine-tuning the pre-trained model with labeled data from downstream tasks. However, in neural machine translation (NMT), we address the problem that the training objective of the bilingual task is far different from the monolingual pre-trained model. This gap leads that only using fine-tuning in NMT can not fully utilize prior language knowledge. In this paper, we propose an APT framework for acquiring knowledge from the pre-trained model to NMT. The proposed approach includes two modules: 1). a dynamic fusion mechanism to fuse task-specific features adapted from general knowledge into NMT network, 2). a knowledge distillation paradigm to learn language knowledge continuously during the NMT training process. The proposed approach could integrate suitable ...
Keyword:
Computation and Language cs.CL
;
FOS Computer and information sciences
URL:
https://dx.doi.org/10.48550/arxiv.1912.01774
https://arxiv.org/abs/1912.01774
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8
Findings of the 2017 conference on machine translation (WMT17)
Huang, Shujian
;
Negri, Matteo
;
Rubino, Raphael
...
In: Bojar, Ondřej orcid:0000-0002-0606-0050 , Chatterjee, Rajen, Federmann, Christian, Graham, Yvette, Haddow, Barry, Huang, Shujian, Huck, Matthias, Koehn, Philipp, Liu, Qun orcid:0000-0002-7000-1792 , Logacheva, Varvara, Monz, Christof, Negri, Matteo, Post, Matt, Rubino, Raphael, Specia, Lucia and Turchi, Marco (2017) Findings of the 2017 conference on machine translation (WMT17). In: Second Conference on Machine Translation (WMT17), 7-11 Sept 2017, Copenhagen, Denmark. ISBN 978-1-945626-96-8 (2017)
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9
Chunk-Based Bi-Scale Decoder for Neural Machine Translation ...
Zhou, Hao
;
Tu, Zhaopeng
;
Huang, Shujian
. - : arXiv, 2017
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10
PQAC-WN: constructing a wordnet for Pre-Qin ancient Chinese [<Journal>]
Chen, Jiajun
[Sonstige];
Li, Bin
[Sonstige];
Huang, Shujian
[Sonstige].
DNB Subject Category Language
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