Multilingual NLP
Aug 2, 2020
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](/~antonis/author/george-mason-nlp/avatar_hu9fb01ed551e807f6417f5d74f9b825b4_111211_270x270_fill_lanczos_center_2.png)
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.
Related
- Machine Translation into Low-resource Language Varieties
- Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties
- SD-QA: Spoken Dialectal Question Answering for the Real World
- Phoneme Recognition through Fine Tuning of Phonetic Representations: a Case Study on Luhya Language Varieties
- Evaluating the Morphosyntactic Well-formedness of Generated Texts
Posts
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.