In recent years,the domain of machine translation has experienced remarkable growth,particularly with the emergence of neural machine translation,which has significantly enhanced both the accuracy and fluency of trans...In recent years,the domain of machine translation has experienced remarkable growth,particularly with the emergence of neural machine translation,which has significantly enhanced both the accuracy and fluency of translation.At the same time,AI also showed its tremendous advancement,with its capabilities now extending to assisting users in a multitude of tasks,including translation,garnering attention across various sectors.In this paper,the author selects representative sentences from both literary and scientific texts,and translates them using two translation software and two AI tools for comparison.The results show that all four translation tools are very efficient and can help with simple translation tasks.However,the accuracy of terminology needs to be improved,and it is difficult to make adjustments based on the characteristics of the target language.It is worth mentioning that one of the advantages of AI is its interactivity,which allows it to modify the translation according to the translator’s needs.展开更多
Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultur...Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.展开更多
An Italian scholar fulfills his potential in China,by being aligned with the country’s vibrant cultural development.“In August 2014,when I set my feet on the land of China,ride-hailing apps like Didi weren’t popula...An Italian scholar fulfills his potential in China,by being aligned with the country’s vibrant cultural development.“In August 2014,when I set my feet on the land of China,ride-hailing apps like Didi weren’t popular in Nanjing yet,and translation software was rarely seen.I didn’t speak Chinese,so even simple tasks like going out was challenging,”said Andrea L.Baldini,a Changjiang Scholar Distinguished Professor at Peking University’s School of Arts,in an exclusive interview with China Today.展开更多
文摘In recent years,the domain of machine translation has experienced remarkable growth,particularly with the emergence of neural machine translation,which has significantly enhanced both the accuracy and fluency of translation.At the same time,AI also showed its tremendous advancement,with its capabilities now extending to assisting users in a multitude of tasks,including translation,garnering attention across various sectors.In this paper,the author selects representative sentences from both literary and scientific texts,and translates them using two translation software and two AI tools for comparison.The results show that all four translation tools are very efficient and can help with simple translation tasks.However,the accuracy of terminology needs to be improved,and it is difficult to make adjustments based on the characteristics of the target language.It is worth mentioning that one of the advantages of AI is its interactivity,which allows it to modify the translation according to the translator’s needs.
基金support provided from the Cooperative Education Fund of China Ministry of Education(201702113002 and 201801193119)Hunan Natural Science Foundation(2018JJ2138)Degree and Graduate Education Reform Project of Hunan Province(JG2018B096)are greatly appreciated by the authors.
文摘Translation software has become an important tool for communication between different languages.People’s requirements for translation are higher and higher,mainly reflected in people’s desire for barrier free cultural exchange.With a large corpus,the performance of statistical machine translation based on words and phrases is limited due to the small size of modeling units.Previous statistical methods rely primarily on the size of corpus and number of its statistical results to avoid ambiguity in translation,ignoring context.To support the ongoing improvement of translation methods built upon deep learning,we propose a translation algorithm based on the Hidden Markov Model to improve the use of context in the process of translation.During translation,our Hidden Markov Model prediction chain selects a number of phrases with the highest result probability to form a sentence.The collection of all of the generated sentences forms a topic sequence.Using probabilities and article sequences determined from the training set,our method again applies the Hidden Markov Model to form the final translation to improve the context relevance in the process of translation.This algorithm improves the accuracy of translation,avoids the combination of invalid words,and enhances the readability and meaning of the resulting translation.
文摘An Italian scholar fulfills his potential in China,by being aligned with the country’s vibrant cultural development.“In August 2014,when I set my feet on the land of China,ride-hailing apps like Didi weren’t popular in Nanjing yet,and translation software was rarely seen.I didn’t speak Chinese,so even simple tasks like going out was challenging,”said Andrea L.Baldini,a Changjiang Scholar Distinguished Professor at Peking University’s School of Arts,in an exclusive interview with China Today.
