Back to the Future: On Potential Histories in NLP

By Zeerak Talat, Anne Lauscher. 2022

5 citations recorded by the source corpus.

Machine learning and NLP require the construction of datasets to train and fine-tune models. In this context, previous work has demon-strated the sensitivity of these data sets. For instance, potential societal biases in this data are likely to be encoded and to be amplified in the models we deploy. In this work, we draw from developments in the field of history and take a novel perspective on these prob-lems: considering datasets and models through the lens of historical fiction surfaces their po-litical nature, and affords re-configuring how we view the past, such that marginalized discourses are surfaced. Building on such in-sights, we argue that contemporary methods for machine learning are prejudiced towards dominant and hegemonic histories. Employ-ing the example of neopronouns, we show that by surfacing marginalized histories within contemporary conditions, we can create models that better represent the lived realities of tra-ditionally marginalized and excluded commu-nities.

Subfields: General Social Sciences, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In General Social Sciences

  1. Zeerak Talat
  2. Yacine Jernite
  3. Alexandra Sasha Luccioni
  4. Jesse Dodge
  5. Su Lin Blodgett
  6. Margaret Mitchell

In Natural Language Processing

  1. Alexandra Sasha Luccioni
  2. Dylan Baker
  3. Margaret Mitchell
  4. Zeerak Talat
  5. Su Lin Blodgett
  6. Irene Solaiman
  7. Yacine Jernite
  8. William Agnew

Citations in the corpus