Much Morphology, Little Data

Par

Garrett Nicolai

University of British Columbia

Résumé:

Computational Linguistics has seen a renaissance in the past 5 years with the advent of deep learning. New benchmarks are regularly set and broken as traditional tasks are supplemented with powerful neural models such as BERT. These methods, however, are very data-hungry, often requiring many millions of lines of text to train. The sparsity imposed by rich inflectional systems exacerbates this problem significantly. 

In this talk, I will describe the current status of computational inflectional morphology, and describe some recent work I have done in creating and exploiting resources in the low resource sphere. 

Biographie :

Garrett Nicolai is a post-doctoral fellow in the Department of Linguistics at UBC, working on problems related to computational linguistics, particularly when data is small. He received his PhD from the Department of Computing Science at the University of Alberta in 2017, before working as a post-doc with the LORELEI project at Johns Hopkins University.