NLP-Learning-Notes
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🧠 NLP笔记，入门概念，基础知识，研究方法，顶会研读
📚 NLP-Learning-Notes
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NLP笔记，入门概念，基础知识，研究方法，顶会研读
作者研究导向：
- 机器翻译
- 知识图谱
🌈 为什么写这份笔记
科研于我是一段孤独的道路
路上总会遇到很多很多人
希望我也有能力可以拉你一把
希望你也愿意和我一起走过一段路程
✨导航
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知识图谱技术
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机器翻译
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- Word Translation Without Parallel Data (Alexis Conneau, ICLR, 2018)
- Unsupervised Machine Translation Using Monolingual Corpora Only (Guillaume Lample, ICLR, 2018)
- Phrase-Based & Neural Unsupervised Machine Translation (Guillaume Lample, EMNLP, 2018)
- Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data (Wei-Jen Ko, ACL, 2021)
- A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation (Shuo Ren, ACL, 2020)
- Multilingual Unsupervised Neural Machine Translation with Denoising Adapters (Ahmet Üstün, EMNLP, 2021)
- Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT (Alexandra, EMNLP, 2020)
- Cross-lingual Language Model Pretraining (Guillaume Lample, NeurIPS, 2019)
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[低资源机器翻译]
- Low-resource Neural Machine Translation with Cross-modal Alignment (Zhe Yang, EMNLP, 2022)
- SMaLL-100: Introducing Shallow Multilingual Machine Translation Model for Low-Resource Languag (Alireza Mohammadshahi, EMNLP, 2022)
- ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine Translation (Zhaocong Li, EMNLP, 2022)
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未分类
- Attention is All You Need (Vaswani, NIPS, 2017)
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模型压缩
- TinyBERT: Distilling BERT for Natural Language Understanding (Xiaoqi Jiao, EMNLP, 2020)
🍉 欢迎批评指正
由于个人水平有限，笔记中难免有笔误甚至概念错误之处，请各位不吝赐教，在issue中提出来。
🌸 关于作者
如遇到问题，请致邮（Email）：[email protected]
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