62 citations · 146 across the 49 of their papers we have counts for
5 papers · 1 filter
Transductive Auxiliary Task Self-Training for Neural Multi-Task Models
Johannes Bjerva, Katharina Kann, Isabelle Augenstein
Multi-task learning and self-training are two common ways to improve a machine learning model's performance in settings with limited training data. Drawing heavily on ideas from th…
Back to the Future -- Sequential Alignment of Text Representations
Johannes Bjerva, Wouter Kouw, Isabelle Augenstein
Language evolves over time in many ways relevant to natural language processing tasks. For example, recent occurrences of tokens 'BERT' and 'ELMO' in publications refer to neural n…
Uncovering Probabilistic Implications in Typological Knowledge Bases
Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell +1
The study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have post…
A Probabilistic Generative Model of Linguistic Typology
Johannes Bjerva, Yova Kementchedjhieva, Ryan Cotterell +1
In the principles-and-parameters framework, the structural features of languages depend on parameters that may be toggled on or off, with a single parameter often dictating the sta…
What do Language Representations Really Represent?
Johannes Bjerva, Robert Östling, Maria Han Veiga +2
A neural language model trained on a text corpus can be used to induce distributed representations of words, such that similar words end up with similar representations. If the cor…