Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation-Reference-Cited by-同舟云学术

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Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation

Published:2019 Issue: Volume: Page:
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Container-title:Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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Author:

Tan Xin,Zhang Longyin,Xiong Deyi,Zhou Guodong

Publisher

Association for Computational Linguistics

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Document-Level Machine Translation with Effective Batch-Level Context Representation;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Summarize While Translating: Universal Model With Parallel Decoding for Summarization and Translation;2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU);2023-12-16

3. Top-down Text-Level Discourse Rhetorical Structure Parsing with Bidirectional Representation Learning;Journal of Computer Science and Technology;2023-09

4. Mongolian-Chinese Machine Translation Based on Text Context Information;2023 8th International Conference on Intelligent Computing and Signal Processing (ICSP);2023-04-21

5. Document-Level Neural Machine Translation With Recurrent Context States;IEEE Access;2023

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