English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The success of pretrained contextual encoders, such as ELMo and BERT, hasbrought a great deal of interest in what these models learn: do they, withoutexplicit supervision, learn to encode meaningful notions of linguisticstructure? If so, how is this structure encoded? To investigate this, weintroduce latent subclass learning (LSL): a modification to existingclassifier-based probing methods that induces a latent categorization (orontology) of the probe's inputs. Without access to fine-grained gold labels,LSL extracts emergent structure from input representations in an interpretableand quantifiable form. In experiments, we find strong evidence of familiarcategories, such as a notion of personhood in ELMo, as well as novelontological distinctions, such as a preference for fine-grained semantic roleson core arguments. Our results provide unique new evidence of emergentstructure in pretrained encoders, including departures from existingannotations which are inaccessible to earlier methods.

Asking without Telling: Exploring Latent Ontologies in Contextual Representations