171 citations · 205 across the 9 of their papers we have counts for
9 papers
MasakhaNER: Named Entity Recognition for African Languages
David Ifeoluwa Adelani, Jade Abbott, Graham Neubig +58
We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named en…
Building Low-Resource NER Models Using Non-Speaker Annotation
Tatiana Tsygankova, Francesca Marini, Stephen Mayhew +1
In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it. Cross-lingual methods h…
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…
Robust Named Entity Recognition with Truecasing Pretraining
Stephen Mayhew, Nitish Gupta, Dan Roth
Although modern named entity recognition (NER) systems show impressive performance on standard datasets, they perform poorly when presented with noisy data. In particular, capitali…
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…
Named Entity Recognition with Partially Annotated Training Data
Stephen Mayhew, Snigdha Chaturvedi, Chen-Tse Tsai +1
Supervised machine learning assumes the availability of fully-labeled data, but in many cases, such as low-resource languages, the only data available is partially annotated. We st…