My name is Yao Fu 符尧
I am a Ph.D. student at the University of Edinburgh (2020-) with Prof. Mirella Lapata.
I finished my M.S. at Columbia University (2018-2020) with Prof. John Cunningham and my B.S. at Peking University (2013-2018) with Prof. Yansong Feng. Before Ph.D., I spent great time visiting Prof. Alexander Rush at Cornell University (2019-2020).
I study machine learning problems rooted from NLP tasks, particularly generation and structured prediction. I derive probabilistic models guided by Bayesian principles, equipped with modern architectures, utilizing efficient inference, and grounded to linguistics and real-world scenarios.
In terms of specific topics, I am interested in
- NLP: Semantics; Text Generation; Structured Prediction
- ML: Generalization; Deep Generative Models; Discrete Latent Structures
Many topics that I’m interested in are covered by the following reading list:
- Deep Generative Models for Natural Language Processing. (github)
- Compositional Generalization in Natural Language Processing. (github)
- NAACL 2021. Noisy Labeled NER with Confidence Estimation. [paper]
- Kun Liu*, Yao Fu*, Chuanqi Tan, Mosha Chen, Ningyu Zhang, Songfang Huang and Sheng Gao. *Equal contribution.
- A confidence estimation method for estimating label noise in NER annotations and a training method based on partial marginalization according to estimated noise.
- ICLR 2021. Probing BERT in Hyperbolic Spaces. [paper][code]
- Boli Chen*, Yao Fu*, Guangwei Xu, Pengjun Xie, Chuanqi Tan, Mosha Chen, Liping Jing. *Equal contribution.
- A Poincare probe for recovering hierarchical structures from contextualized representations. Applied to probing syntax and sentiment in BERT.
- ICLR 2021. Prototypical Representation Learning for Relation Extraction. [paper]
- Ning Ding, Xiaobin Wang, Yao Fu, Guangwei Xu, Rui Wang, Pengjun Xie, Ying Shen, Fei Huang, Hai-Tao Zheng, Rui Zhang
- A representation learning method for embedding relation prototypes on hyperspheres. Applied to supervised, semi-supervised, and few-shot relational learning.
- AAAI 2021. Nested Named Entity Recognition with Partially Observed TreeCRFs. [paper][code]
- Yao Fu*, Chuanqi Tan*, Mosha Chen, Songfang Huang, Fei Huang. *Equal contribution.
- A Masked Inside algorithm for efficient partial marginalization of TreeCRFs. Applied to Nested NER.
- NeurIPS 2020. Latent Template Induction with Gumbel-CRFs. [paper][code]
- Yao Fu, Chuanqi Tan, Mosha Chen, Bin Bi, Yansong Feng and Alexander Rush.
- A Gumbel-FFBS algorithm for reparameterizing and relaxing CRFs. Applied to controllable text generation with latent templates.
- NeurIPS 2019. Paraphrase Generation with Latent Bag of Words. [paper][code]
- Yao Fu, Yansong Feng and John Cunningham.
- A differentiable planning and realization model with latent bag of words by Gumbel-topK reparameterization. Applied to paraphrase generation.
- INLG 2019. Rethinking Text Attribute Transfer: A Lexical Analysis. [paper][code]
- Yao Fu, Hao Zhou, Jiaze Chen and Lei Li.
- A series of text mining algorithms for discovering words with strong influence on classification. Applied to analysing text attribute transfer models.
- NAACL 2018. Natural Answer Generation with Heterogeneous Memory. [paper]
- Yao Fu and Yansong Feng.
- An attention mechanism fusing information from different source of knowledge. Applied to answer sentence generation.
- Jan 20 - Oct 20. Alibaba Damo Academy. Natural Language Processing Research Intern. Beijing and Hangzhou.
- May 19 - Aug 19. Tencent AI Lab. Natural Language Processing Research Intern. Seattle.
- Dec 17 - Aug 18. Bytedance AI Lab. Natural Language Processing Research Intern. Beijing.