309 citations · 439 across the 15 of their papers we have counts for
5 papers · 1 filter
Which *BERT? A Survey Organizing Contextualized Encoders
Patrick Xia, Shijie Wu, Benjamin Van Durme
Pretrained contextualized text encoders are now a staple of the NLP community. We present a survey on language representation learning with the aim of consolidating a series of sha…
SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection
Ekaterina Vylomova, Jennifer White, Elizabeth Salesky +25
A broad goal in natural language processing (NLP) is to develop a system that has the capacity to process any natural language. Most systems, however, are developed using data from…
The Paradigm Discovery Problem
Alexander Erdmann, Micha Elsner, Shijie Wu +2
This work treats the paradigm discovery problem (PDP), the task of learning an inflectional morphological system from unannotated sentences. We formalize the PDP and develop evalua…
Applying the Transformer to Character-level Transduction
Shijie Wu, Ryan Cotterell, Mans Hulden
The transformer has been shown to outperform recurrent neural network-based sequence-to-sequence models in various word-level NLP tasks. Yet for character-level transduction tasks,…
Are All Languages Created Equal in Multilingual BERT?
Shijie Wu, Mark Dredze
Multilingual BERT (mBERT) trained on 104 languages has shown surprisingly good cross-lingual performance on several NLP tasks, even without explicit cross-lingual signals. However,…