activity
20202022
most citedSTEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

47 citations · 97 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2022

ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select

Yuchen Zhuang, Yinghao Li, Jerry Junyang Cheung +5

We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and mod…

cs.LG202139 cited

WRENCH: A Comprehensive Benchmark for Weak Supervision

Jieyu Zhang, Yue Yu, Yinghao Li +4

Recent Weak Supervision (WS) approaches have had widespread success in easing the bottleneck of labeling training data for machine learning by synthesizing labels from multiple pot…

cs.CL20211 cited

BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition

Yinghao Li, Pranav Shetty, Lucas Liu +2

We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak sup…

cs.CL2020

Denoising Multi-Source Weak Supervision for Neural Text Classification

Wendi Ren, Yinghao Li, Hanting Su +3

We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challe…

cs.CL202010 cited

Transformer-Based Neural Text Generation with Syntactic Guidance

Yinghao Li, Rui Feng, Isaac Rehg +1

We study the problem of using (partial) constituency parse trees as syntactic guidance for controlled text generation. Existing approaches to this problem use recurrent structures,…

cs.CL202047 cited

STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths

Yue Yu, Yinghao Li, Jiaming Shen +3

Taxonomies are important knowledge ontologies that underpin numerous applications on a daily basis, but many taxonomies used in practice suffer from the low coverage issue. We stud…