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On the Copying Behaviors of Pre-Training for Neural Machine Translation ...
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Norm-Based Curriculum Learning for Neural Machine Translation ...
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Shared-Private Bilingual Word Embeddings for Neural Machine Translation ...
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Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling ...
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Towards Bidirectional Hierarchical Representations for Attention-Based Neural Machine Translation ...
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A Relationship: Word Alignment, Phrase Table, and Translation Quality
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iSentenizer-μ: Multilingual Sentence Boundary Detection Model
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Unsupervised Quality Estimation Model for English to German Translation and Its Application in Extensive Supervised Evaluation
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Abstract:
With the rapid development of machine translation (MT), the MT evaluation becomes very important to timely tell us whether the MT system makes any progress. The conventional MT evaluation methods tend to calculate the similarity between hypothesis translations offered by automatic translation systems and reference translations offered by professional translators. There are several weaknesses in existing evaluation metrics. Firstly, the designed incomprehensive factors result in language-bias problem, which means they perform well on some special language pairs but weak on other language pairs. Secondly, they tend to use no linguistic features or too many linguistic features, of which no usage of linguistic feature draws a lot of criticism from the linguists and too many linguistic features make the model weak in repeatability. Thirdly, the employed reference translations are very expensive and sometimes not available in the practice. In this paper, the authors propose an unsupervised MT evaluation metric using universal part-of-speech tagset without relying on reference translations. The authors also explore the performances of the designed metric on traditional supervised evaluation tasks. Both the supervised and unsupervised experiments show that the designed methods yield higher correlation scores with human judgments.
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Keyword:
Research Article
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URL: http://www.ncbi.nlm.nih.gov/pubmed/24892086 https://doi.org/10.1155/2014/760301 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4032676
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A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation
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Unsupervised Chunking Based on Graph Propagation from Bilingual Corpus
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