How we got here: Short-scale change in identity labels for trans, cis, and non-binary people in the 2000s

@inproceedings{Zimman2020HowWG,
  title={How we got here: Short-scale change in identity labels for trans, cis, and non-binary people in the 2000s},
  author={Lal Zimman and Will Hayworth},
  year={2020}
}
Though understudied in research on language variation and change, the lexicon is a crucial domain for sociopolitical transformations of language. This paper presents a corpus-based sociolinguistic analysis of changes in terms for transgender, cisgender, and non-binary individuals in four online communities on the social media blogging site, LiveJournal.com – one for trans women, one for trans men, one for non-binary people, and another for transgender people in general – that were popular in… 

"I'm sorry to hear that": finding bias in language models with a holistic descriptor dataset

TLDR
This work presents a new, more inclusive dataset, H OLISTIC B IAS, which consists of nearly 600 descriptor terms across 13 different demographic axes, and demonstrates that this dataset is highly efficacious for measuring previously unmea-surable biases in token likelihoods and generations from language models, as well as in an offensiveness classi fier.

Perturbation Augmentation for Fairer NLP

TLDR
It is shown that language models pre-trained on demographically perturbed corpora are more fair, at least, according to the current best metrics for measuring model fairness, and that improved fairness does not come at the expense of accuracy.

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