Speciesist Language and Nonhuman Animal Bias in English Masked Language Models

Autor:	Takeshita, Masashi, Rzepka, Rafal, Araki, Kenji
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
DOI:	10.1016/j.ipm.2022.103050
Popis:	Various existing studies have analyzed what social biases are inherited by NLP models. These biases may directly or indirectly harm people, therefore previous studies have focused only on human attributes. However, until recently no research on social biases in NLP regarding nonhumans existed. In this paper, we analyze biases to nonhuman animals, i.e. speciesist bias, inherent in English Masked Language Models such as BERT. We analyzed speciesist bias against 46 animal names using template-based and corpus-extracted sentences containing speciesist (or non-speciesist) language. We found that pre-trained masked language models tend to associate harmful words with nonhuman animals and have a bias toward using speciesist language for some nonhuman animal names. Our code for reproducing the experiments will be made available on GitHub. Comment: This paper is an accepted manuscript for publication in Information Processing & Management
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2203.05140 Zobrazit plný text záznamu View this record from Arxiv