Today the development of information technology changes people's daily life in many aspects, including the translationmarket. The application of computer aided translation(CAT) greatly improves the efficiency and ...Today the development of information technology changes people's daily life in many aspects, including the translationmarket. The application of computer aided translation(CAT) greatly improves the efficiency and quality of translation work. Thispaper tries to introduce the development of CAT technology and the application of CAT on the Internet and gives some suggestionson CAT teaching.展开更多
Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of...Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of data prepared in advance, which is often challenging in real-world applications, such as streaming data and concept drift. For this reason, incremental learning (continual learning) has attracted increasing attention from scholars. However, incremental learning is associated with the challenge of catastrophic forgetting: the performance on previous tasks drastically degrades after learning a new task. In this paper, we propose a new strategy to alleviate catastrophic forgetting when neural networks are trained in continual domains. Specifically, two components are applied: data translation based on transfer learning and knowledge distillation. The former translates a portion of new data to reconstruct the partial data distribution of the old domain. The latter uses an old model as a teacher to guide a new model. The experimental results on three datasets have shown that our work can effectively alleviate catastrophic forgetting by a combination of the two methods aforementioned.展开更多
海明威的短篇小说“Cat in the Rain”因其视角和主题的独特性而具有不可忽视的文学价值,不少翻译者译过该作品。从词汇、句法和语篇三方面对曹雍先生和张祥麟先生的译本进行比较和分析,以期提高文学翻译的理解能力和表达技巧,从而更好...海明威的短篇小说“Cat in the Rain”因其视角和主题的独特性而具有不可忽视的文学价值,不少翻译者译过该作品。从词汇、句法和语篇三方面对曹雍先生和张祥麟先生的译本进行比较和分析,以期提高文学翻译的理解能力和表达技巧,从而更好地指导翻译实践。展开更多
文摘Today the development of information technology changes people's daily life in many aspects, including the translationmarket. The application of computer aided translation(CAT) greatly improves the efficiency and quality of translation work. Thispaper tries to introduce the development of CAT technology and the application of CAT on the Internet and gives some suggestionson CAT teaching.
文摘Recently, deep convolutional neural networks (DCNNs) have achieved remarkable results in image classification tasks. Despite convolutional networks’ great successes, their training process relies on a large amount of data prepared in advance, which is often challenging in real-world applications, such as streaming data and concept drift. For this reason, incremental learning (continual learning) has attracted increasing attention from scholars. However, incremental learning is associated with the challenge of catastrophic forgetting: the performance on previous tasks drastically degrades after learning a new task. In this paper, we propose a new strategy to alleviate catastrophic forgetting when neural networks are trained in continual domains. Specifically, two components are applied: data translation based on transfer learning and knowledge distillation. The former translates a portion of new data to reconstruct the partial data distribution of the old domain. The latter uses an old model as a teacher to guide a new model. The experimental results on three datasets have shown that our work can effectively alleviate catastrophic forgetting by a combination of the two methods aforementioned.
