Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with a low agreement. On the other hand, the popular automatic MT evaluation methods have some weaknesses. Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as the source. Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicate and apply to other language pairs easily. Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks. In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances in different languages. Firstly, we design augmented factors to yield highly accurate evaluation. Secondly, we design tunable evaluation models, ...
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My research topics include Machine Translation and NLP. I had research project experiences at the University of Macau, University of Amsterdam and Dublin City University. I hold Master degree in CS with Excellent Award for my thesis, Bachelor degree in Math. My team won National Second Prize in Postgraduate Mathematical Modeling Contest of China.
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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with a low agreement. On the other hand, the popular automatic MT evaluation methods have some weaknesses. Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as the source. Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicate and apply to other language pairs easily. Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks. In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances in different languages. Firstly, we design augmented factors to yield highly accurate evaluation. Secondly, we design tunable evaluation models, . 136 pp. Englisch. N° de réf. du vendeur 9786202199810
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Han LifengMy research topics include Machine Translation and NLP. I had research project experiences at the University of Macau, University of Amsterdam and Dublin City University. I hold Master degree in CS with Excellent Award for . N° de réf. du vendeur 385934234
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Paperback. Etat : Brand New. 136 pages. 8.66x5.91x0.31 inches. In Stock. N° de réf. du vendeur zk6202199814
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with a low agreement. On the other hand, the popular automatic MT evaluation methods have some weaknesses. Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as the source. Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicate and apply to other language pairs easily. Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks. In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances in different languages. Firstly, we design augmented factors to yield highly accurate evaluation. Secondly, we design tunable evaluation models, .Books on Demand GmbH, Überseering 33, 22297 Hamburg 136 pp. Englisch. N° de réf. du vendeur 9786202199810
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine translation (MT) was developed as one of the hottest research topics in the natural language processing (NLP) literature. One important issue in MT is that how to evaluate the MT system reasonably and tell us whether the translation system makes an improvement or not. The traditional manual judgment methods are expensive, time-consuming, unrepeatable, and sometimes with a low agreement. On the other hand, the popular automatic MT evaluation methods have some weaknesses. Firstly, they tend to perform well on the language pairs with English as the target language, but weak when English is used as the source. Secondly, some methods rely on many additional linguistic features to achieve good performance, which makes the metric unable to replicate and apply to other language pairs easily. Thirdly, some popular metrics utilize incomprehensive factors, which result in low performance on some practical tasks. In this thesis, to address the existing problems, we design novel MT evaluation methods and investigate their performances in different languages. Firstly, we design augmented factors to yield highly accurate evaluation. Secondly, we design tunable evaluation models, . N° de réf. du vendeur 9786202199810
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Taschenbuch. Etat : Neu. LEPOR: An Augmented Machine Translation Evaluation Metric | MT Evaluation, Quality Estimation, and Multilingual Treebanks | Lifeng Han | Taschenbuch | 136 S. | Englisch | 2017 | LAP LAMBERT Academic Publishing | EAN 9786202199810 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. N° de réf. du vendeur 110944267
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