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.
- Reducing Confusion in Active Learning for Part-Of-Speech Tagging
- X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models
- It's not a Non-Issue: Negation as a Source of Error in Machine Translation
- Comparison of Interactive Knowledge Base Spelling Correction Models for Low-Resource Languages
- Automatic Extraction of Rules Governing Morphological Agreement