基本信息
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Bio
Research
I am broadly interested in Natural Language Processing and Machine Learning methods that can be used to automatically learn the underlying knowledge in languages.
On the methodology side, I am intrigued by elegant models with end-to-end training, such as the capability of Deep Neural Networks to learn powerful representations without explicitly injecting human knowledge. I am also interested in structured prediction models that can handle complex output spaces since NLP problems usually have structured outputs.
On the application side, I am very interested in extracting and understanding knowledge behind languages, such as representation learning, information extraction, reading comprehension, etc.
Recently, my proposed thesis research focuses on learning deep representations for low-resource / zero-resource cross-lingual model transfer.
I am broadly interested in Natural Language Processing and Machine Learning methods that can be used to automatically learn the underlying knowledge in languages.
On the methodology side, I am intrigued by elegant models with end-to-end training, such as the capability of Deep Neural Networks to learn powerful representations without explicitly injecting human knowledge. I am also interested in structured prediction models that can handle complex output spaces since NLP problems usually have structured outputs.
On the application side, I am very interested in extracting and understanding knowledge behind languages, such as representation learning, information extraction, reading comprehension, etc.
Recently, my proposed thesis research focuses on learning deep representations for low-resource / zero-resource cross-lingual model transfer.
Research Interests
Papers共 44 篇Author StatisticsCo-AuthorSimilar Experts
By YearBy Citation主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
arxiv(2024)
Cited0Views0Bibtex
0
0
CoRR (2024)
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0
0
CoRR (2024)
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0
0
CoRR (2024)
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0
0
arXiv (Cornell University) (2023)
CoRR (2023)
Conference of the European Chapter of the Association for Computational Linguistics (2023): 190-208
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0
0
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