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1
Variance-Aware Machine Translation Test Sets ...
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2
Difficulty-Aware Machine Translation Evaluation ...
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3
On the Copying Behaviors of Pre-Training for Neural Machine Translation ...
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4
Document Graph for Neural Machine Translation ...
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5
Norm-Based Curriculum Learning for Neural Machine Translation ...
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6
Document Graph for Neural Machine Translation ...
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7
Shared-Private Bilingual Word Embeddings for Neural Machine Translation ...
Liu, Xuebo; Wong, Derek F.; Liu, Yang. - : arXiv, 2019
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8
Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling ...
Wan, Yu; Yang, Baosong; Wong, Derek F.. - : arXiv, 2019
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9
Towards Bidirectional Hierarchical Representations for Attention-Based Neural Machine Translation ...
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10
A Relationship: Word Alignment, Phrase Table, and Translation Quality
Tian, Liang; Wong, Derek F.; Chao, Lidia S.. - : Hindawi Publishing Corporation, 2014
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11
Chinese Unknown Word Recognition for PCFG-LA Parsing
Huang, Qiuping; He, Liangye; Wong, Derek F.. - : Hindawi Publishing Corporation, 2014
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12
iSentenizer-μ: Multilingual Sentence Boundary Detection Model
Wong, Derek F.; Chao, Lidia S.; Zeng, Xiaodong. - : Hindawi Publishing Corporation, 2014
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13
Unsupervised Quality Estimation Model for English to German Translation and Its Application in Extensive Supervised Evaluation
Han, Aaron L.-F.; Wong, Derek F.; Chao, Lidia S.. - : Hindawi Publishing Corporation, 2014
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14
A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation
Wang, Longyue; Wong, Derek F.; Chao, Lidia S.. - : Hindawi Publishing Corporation, 2014
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15
Unsupervised Chunking Based on Graph Propagation from Bilingual Corpus
Zhu, Ling; Wong, Derek F.; Chao, Lidia S.. - : Hindawi Publishing Corporation, 2014
Abstract: This paper presents a novel approach for unsupervised shallow parsing model trained on the unannotated Chinese text of parallel Chinese-English corpus. In this approach, no information of the Chinese side is applied. The exploitation of graph-based label propagation for bilingual knowledge transfer, along with an application of using the projected labels as features in unsupervised model, contributes to a better performance. The experimental comparisons with the state-of-the-art algorithms show that the proposed approach is able to achieve impressive higher accuracy in terms of F-score.
Keyword: Research Article
URL: https://doi.org/10.1155/2014/401943
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3977424
http://www.ncbi.nlm.nih.gov/pubmed/24772017
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