activity
20192021
most citedPrototypical Representation Learning for Relation Extraction

38 citations · 103 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL20211 cited

Noisy-Labeled NER with Confidence Estimation

Kun Liu, Yao Fu, Chuanqi Tan +4

Recent studies in deep learning have shown significant progress in named entity recognition (NER). Most existing works assume clean data annotation, yet a fundamental challenge in…

cs.CL202119 cited

Probing BERT in Hyperbolic Spaces

Boli Chen, Yao Fu, Guangwei Xu +4

Recently, a variety of probing tasks are proposed to discover linguistic properties learned in contextualized word embeddings. Many of these works implicitly assume these embedding…

cs.CL202138 cited

Prototypical Representation Learning for Relation Extraction

Ning Ding, Xiaobin Wang, Yao Fu +7

Recognizing relations between entities is a pivotal task of relational learning. Learning relation representations from distantly-labeled datasets is difficult because of the abund…

cs.CL20202 cited

Nested Named Entity Recognition with Partially-Observed TreeCRFs

Yao Fu, Chuanqi Tan, Mosha Chen +2

Named entity recognition (NER) is a well-studied task in natural language processing. However, the widely-used sequence labeling framework is difficult to detect entities with nest…

cs.CL20202 cited

Latent Template Induction with Gumbel-CRFs

Yao Fu, Chuanqi Tan, Bin Bi +3

Learning to control the structure of sentences is a challenging problem in text generation. Existing work either relies on simple deterministic approaches or RL-based hard structur…

cs.CL202035 cited

Paraphrase Generation with Latent Bag of Words

Yao Fu, Yansong Feng, John P. Cunningham

Paraphrase generation is a longstanding important problem in natural language processing. In addition, recent progress in deep generative models has shown promising results on disc…