activity
20192021
most citedCross-Lingual Ability of Multilingual BERT: An Empirical Study

171 citations · 192 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CL202118 cited

UCPhrase: Unsupervised Context-aware Quality Phrase Tagging

Xiaotao Gu, Zihan Wang, Zhenyu Bi +4

Identifying and understanding quality phrases from context is a fundamental task in text mining. The most challenging part of this task arguably lies in uncommon, emerging, and dom…

cs.CL20203 cited

Extending Multilingual BERT to Low-Resource Languages

Zihan Wang, Karthikeyan K, Stephen Mayhew +1

Multilingual BERT (M-BERT) has been a huge success in both supervised and zero-shot cross-lingual transfer learning. However, this success has focused only on the top 104 languages…

cs.CL2019171 cited

Cross-Lingual Ability of Multilingual BERT: An Empirical Study

Karthikeyan K, Zihan Wang, Stephen Mayhew +1

Recent work has exhibited the surprising cross-lingual abilities of multilingual BERT (M-BERT) -- surprising since it is trained without any cross-lingual objective and with no ali…

cs.CL2019

CrossWeigh: Training Named Entity Tagger from Imperfect Annotations

Zihan Wang, Jingbo Shang, Liyuan Liu +3

Everyone makes mistakes. So do human annotators when curating labels for named entity recognition (NER). Such label mistakes might hurt model training and interfere model compariso…

cs.CL2019

Raw-to-End Name Entity Recognition in Social Media

Liyuan Liu, Zihan Wang, Jingbo Shang +5

Taking word sequences as the input, typical named entity recognition (NER) models neglect errors from pre-processing (e.g., tokenization). However, these errors can influence the m…

cs.CL2019

Discriminative Topic Mining via Category-Name Guided Text Embedding

Yu Meng, Jiaxin Huang, Guangyuan Wang +4

Mining a set of meaningful and distinctive topics automatically from massive text corpora has broad applications. Existing topic models, however, typically work in a purely unsuper…