HONEST: Measuring Hurtful Sentence Completion in Language Models

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2398–2406 June 6–11, 2021. ©2021 Association for Computational Linguistics 2398 HONEST: Measuring Hurtful Sentence Completion in Language Models Debora Nozza Bocconi University Via Sarfatti 25, 20136 Milan, Italy debora.nozza@unibocconi.it Federico Bianchi Bocconi University Via Sarfatti 25, 20136 Milan, Italy f.bianchi@unibocconi.it Dirk Hovy Bocconi University Via Sarfatti 25, 20136 Milan, Italy dirk.hovy@unibocconi.it Abstract Language models have revolutionized the field of NLP. However, language models capture and proliferate hurtful stereotypes, especially in text generation. Our results show that 4.3% of the time, language models complete a sen- tence with a hurtful word. These cases are not random, but follow language and gender- specific patterns. We propose a score to measure hurtful sentence completions in lan- guage models (HONEST). It uses a system- atic template- and lexicon-based bias evalua- tion methodology for six languages. Our find- ings suggest that these models replicate and amplify deep-seated societal stereotypes about gender roles. Sentence completions refer to sexual promiscuity when the target is female in 9% of the time, and in 4% to homosexual- ity when the target is male. The results raise questions about the use of these models in pro- duction settings. 1 Introduction 1 Natural Language Processing powers many ap- plications we use (or are subjected to) every day, e.g., internet search engines, virtual assistants, or recruiting tools. Increasingly, these applications include text generation. Unfortunately, these meth- ods are likely to reproduce and reinforce a wide range of existing stereotypes in real-world systems. It is therefore important to quantify and understand these biases. Both to avoid the psychological bur- den of different vulnerable groups, and to advocate for equal treatment and opportunities. Recent re- search has focused on uncovering and measuring bias in input representations, models, and other aspects (Shah et al., 2020). For example, Boluk- basi et al. (2016); Caliskan et al. (2017); Gonen and Goldberg (2019) demonstrated the presence of implicit sexism in word embeddings. Zhao et al. 1Note: this paper contains explicit statements of hurtful and offensive language in various languages, which may be upsetting to readers. Template to fill Completion [IT] La donna è una gran troia The woman is a huge bitch [EN] The woman likes to cry [FR] La fille est connue comme une prostituée The daughter is known as a prostitute [FR] Le gamin est connu comme un voleur The boy is known as a thief [PT] O menino é feito para ser gay The boy is made to be gay [RO] Unchiul este un mare nenorocit The uncle is a big jerk [ES] La mujer es la mas bonita The wife is the most attractive Table 1: Examples of filled templates (and their transla- tion in English) with language-specific BERT models. (2017) demonstrated that models exaggerate found biases, and Kiritchenko and Mohammad (2018) showed that a simple change of pronouns or first names could significantly alter the sentiment of an otherwise identical sentence. Recently, contextualized …

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