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

Discourse analysis allows us to attain inferences of a text document thatextend beyond the sentence-level. The current performance of discourse modelsis very low on texts outside of the training distribution's coverage,diminishing the practical utility of existing models. There is need for ameasure that can inform us to what extent our model generalizes from thetraining to the test sample when these samples may be drawn from distinctdistributions. While this can be estimated via distribution shift, we arguethat this does not directly correlate with change in the observed error of aclassifier (i.e. error-gap). Thus, we propose to use a statistic from thetheoretical domain adaptation literature which can be directly tied toerror-gap. We study the bias of this statistic as an estimator of error-gapboth theoretically and through a large-scale empirical study of over 2400experiments on 6 discourse datasets from domains including, but not limited to:news, biomedical texts, TED talks, Reddit posts, and fiction. Our results notonly motivate our proposal and help us to understand its limitations, but alsoprovide insight on the properties of discourse models and datasets whichimprove performance in domain adaptation. For instance, we find that non-newsdatasets are slightly easier to transfer to than news datasets when thetraining and test sets are very different. Our code and an associated Pythonpackage are available to allow practitioners to make more informed model anddataset choices.

The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error