我不死扛
发表于 2025-3-25 03:55:37
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Nonthreatening
发表于 2025-3-25 11:10:46
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encyclopedia
发表于 2025-3-25 13:02:34
Transfer Learning for Chinese-Lao Neural Machine Translation with Linguistic Similarity,guistic differences, resulting in poor performance of Chinese-Lao neural machine translation (NMT) task. However, compared with the Chinese-Lao language pair, there are considerable cross-lingual similarities between Thai-Lao languages. According to these features, we propose a novel NMT approach. W
出生
发表于 2025-3-25 16:21:14
MTNER: A Corpus for Mongolian Tourism Named Entity Recognition,zation names. However, there is still a lack of data to identify travel-related named entities, especially in Mongolian. In this paper, we introduce a newly corpus for Mongolian Tourism Named Entity Recognition (MTNER), consisting of 16,000 sentences annotated with 18 entity types. We trained in-dom
草率女
发表于 2025-3-25 23:27:06
Unsupervised Machine Translation Quality Estimation in Black-Box Setting,ence. QE is an important component in making machine translation useful in real-world applications. Existing approaches require large amounts of expert annotated data. Recently, there are some trials to perform QE in an unsupervised manner, but these methods are based on glass-box features which dem
arabesque
发表于 2025-3-26 03:14:16
YuQ: A Chinese-Uyghur Medical-Domain Neural Machine Translation Dataset Towards Knowledge-Driven,NNs) require a large amount of training data with a high-quality annotation which is not available or expensive in the field of the medical domain. The research of medical domain neural machine translation (NMT) is largely limited due to the lack of parallel sentences that consist of medical domain
Thrombolysis
发表于 2025-3-26 05:38:11
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Osteoarthritis
发表于 2025-3-26 09:26:53
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使饥饿
发表于 2025-3-26 14:48:38
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Employee
发表于 2025-3-26 17:53:43
Tsinghua University Neural Machine Translation Systems for CCMT 2020,the Chinese . English translation tasks. Our systems are based on Transformer architectures and we verified that deepening the encoder can achieve better results. All models are trained in a distributed way. We employed several data augmentation methods, including knowledge distillation, back-transl