Talking about other people: an endless range of possibilities

By Emiel van Miltenburg, Desmond Elliott, Piek T. J. M. Vossen. 2018

14 citations recorded by the source corpus.

Image description datasets, such as Flickr30K and MS COCO, show a high degree of variation in the ways that crowd-workers talk about the world. Although this gives us a rich and diverse collection of data to work with, it also introduces uncertainty about how the world should be described. This paper shows the extent of this uncertainty in the PEOPLE-domain. We present a taxonomy of different ways to talk about other people. This taxonomy serves as a reference point to think about how other people should be described, and can be used to classify and compute statistics about labels applied to people.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

“I’m sorry to hear that”: Finding New Biases in Language Models with a Holistic Descriptor Dataset (2022, background): “Since people can refer to themselves and others in an endless number of ways (Van Miltenburg et al., 2018), and since people references are prone to change over time (Smith, 1992; Galinsky et al., 2003; Haller et al., 2006; Zimman and Hayworth, 2020), we have taken inspiration from calls to make…”
Understanding and Evaluating Racial Biases in Image Captioning (2021, background): “Along with many ethical concerns [56, 66], these dataset biases are problematic because they can propagate into models [15, 17].”
"I'm sorry to hear that": finding bias in language models with a holistic descriptor dataset (2022, background): “Since we can refer to ourselves and others in an endless number of ways (Van Miltenburg et al., 2018), and since people references are prone to change over time (Smith, 1992; Galinsky et al., 2003; Haller et al., 2006; Zimman and Hayworth, 2020), we have taken inspiration from calls to make model…”