Multilingual NLP
An exciting research direction that we pursue at GMU NLP is building multi-lingual and polyglot systems. The languages of the world often share similar characteristics, and training systems cross-lingually allows us to leverage these similarities and overcome data scarcity issues.
George Mason NLP
The Natural Language Processing group at George Mason University. We work on multilingual models, on and on building robust NLP systems, especially for low-resource and endangered languages.
Posts
Predicting Performance for Natural Language Processing Tasks
This is a post regarding our paper that will be presented at ACL 2020. tl;dr: You can use previously published results to get an estimation of the performance on a new experiment, before running it!
Should All Cross-Lingual Embeddings Speak English?
This is a post regarding our paper that got accepted at ACL 2020.
Word embeddings are ubiquitous in modern NLP, from static ones (like word2vec or fasttext) to contextual representations obtained from ELMo, BERT, and other models.
A note on evaluating multilingual benchmarks
A note on evaluating multilingual benchmarks Antonis Anastasopoulos, December 2019. tl;dr: Be careful when reporting averages for multilingual benchmarks, especially if making claims about multilinguality. In addition, averaging by language family can provide additional insights.