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Titlebook: Machine Translation; 12th China Workshop, Muyun Yang,Shujie Liu Conference proceedings 2016 Springer Nature Singapore Pte Ltd. 2016 natural

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楼主: dilate
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BLEUS-syn: Cilin-Based Smoothed BLEU,st, that of Youdao is the second, and that of Bing is the worst. Using BLEUS-syn can greatly enhance the performance of traditional BLEUS evaluation. It makes the Baidu BLEUS value improve 6.81%, Youdao improve 6.98%, Google 7.82%, and Bing 7.55%.
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Learning Bilingual Sentence Representations for Quality Estimation of Machine Translation, then we compute the weighted cosine distance of the two vectors to estimate the translation quality of the target sentence. Our experimental results show that our model improve the performance over a baseline system with 17 features in the English-to-Spanish sentence-level quality estimation task of WMT15.
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Research on the Calculation Method of Semantic Similarity Based on Concept Hierarchy,cepts are weighted differently, and the theory and methods of semantic similarity based on concept hierarchy is given. Finally, similarity computing model is prospected. Experimental results show the model has a high computational accuracy.
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1865-0929 ey deal with statistical machine translation, hybrid machine translation, machine translation evaluation, post editing, alignment, and inducing bilingual knowledge from corpora. .978-981-10-3634-7978-981-10-3635-4Series ISSN 1865-0929 Series E-ISSN 1865-0937
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Pivot-Based Semantic Splicing for Neural Machine Translation,e language to generate its semantic representation during the encoding process. We integrated it into the NMT architecture. Experiments on the English-Japanese translation task show that our model achieves a substantial improvement by up to 22.9% (3.74 BLEU) over the baseline.
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Re-ranking for Bilingual Lexicon Extraction with Bi-directional Linear Transformation from Comparabsed re-ranking method by combining the two direction linear score. The experimental results confirm that the proposed solution can achieve a significant improvement of 69% in the precision at Top-1 over the unidirectional baseline approach on the English-to-Chinese bilingual lexicon extraction task.
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Classifying Commas for Patent Machine Translation,rms well in classifying commas. On the other hand, the classifiers show some differences. We also draw the conclusion that identifying commas is actually able to improve the quality of translation outputs.
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1865-0929 6, held in Urumqi, China, in August 2016.. The 10 English papers presented in this volume were carefully reviewed and selected from 76 submissions. They deal with statistical machine translation, hybrid machine translation, machine translation evaluation, post editing, alignment, and inducing biling
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