Challenge: Various languages, such as Spanish, Hebrew, or French, have different words to distinguish between singular "you" and plural "you".
Approach: They train a model to distinguish between the single/plural ‘you’ in English using in-domain training.
Outcome: The proposed model achieves reasonable accuracy, but there is room for improvement in the domain-transfer scenario.

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Detecting Independent Pronoun Bias with Partially-Synthetic Data Generation (2020.emnlp-main)

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Challenge: linguistic differences between English pronouns that are not inherently biased can become biases in some machine learning models.
Approach: They propose a method to detect bias by alternating pronouns in different contexts.
Outcome: The proposed method can be used to detect bias in language models and for text generation more broadly.
Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution (2022.naacl-main)

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Challenge: a new benchmark evaluates coreference resolution systems' ability to recognize singular personal "they" we find that current systems overwhelmingly choose to resolve "they's" correctly to a singular entity or to 'a group'
Approach: They propose to evaluate coreference resolution systems for singular personal "they" they use WinoNB schemas to evaluate whether they can correctly resolve singular "they".
Outcome: The proposed benchmark evaluates coreference resolution systems for singular personal "they" they show that they are biased toward resolving "they", not "them"
A Checkpoint on Multilingual Misogyny Identification (2022.acl-srw)

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Challenge: a study on hate speech against minorities in Italian tweets found that 1 women are the most targeted group.
Approach: They propose to train monolingual transformers and multilingual transformer models with monolingual data in English, Italian, and Spanish to detect misogyny in tweets.
Outcome: The proposed model achieves state-of-the-art on English, Italian, and Spanish.
MISGENDERED: Limits of Large Language Models in Understanding Pronouns (2023.acl-long)

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Challenge: excluding non-binary gender identities can perpetuate harm against non-bisexual individuals through exclusion and marginalization.
Approach: They propose a framework for evaluating large language models’ ability to correctly use preferred pronouns.
Outcome: The proposed framework evaluates language models' ability to correctly use preferred pronouns in English.
They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking (C18-1)

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Challenge: Unlike singular mentions each of which represents one entity, plural mentions stand for multiple entities.
Approach: They propose a novel coreference resolution algorithm that selectively creates clusters to handle both singular and plural mentions and a deep learning-based entity linking model that jointly handles both types of mentions through multi-task learning.
Outcome: The proposed model outperforms existing models designed for singular mentions and plural mentions.
How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns (2022.naacl-main)

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Challenge: a recent study shows that gender-neutral pronouns are not associated with processing difficulties . linguistic scholars have observed how technology has altered the course of language evolution .
Approach: They show that gender-neutral pronouns in Danish, English and Swedish are not associated with processing difficulties.
Outcome: a new study shows that gender-neutral pronouns are not associated with human processing difficulties . the findings suggest that such conservativity in language models may limit widespread adoption .
What about “em”? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns (2023.acl-long)

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Challenge: Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals.
Approach: They compare 3rd-person pronoun translations to five other languages . they propose to address gender exclusivity in future research .
Outcome: The proposed method compares translations of gendered vs. gender-neutral pronouns from english to five other languages and vice versa.
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorials (2021.naacl-tutorials)

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Challenge: NAACL 2021 tutorials session is a conference for researchers to present on a topic of importance . a total of 35 tutorial submissions were received, of which 6 were selected for presentation .
Approach: NAACL 2021 tutorials session is organized to give conference attendees a comprehensive introduction from expert researchers to a topic of importance drawn from our research field.
Outcome: the tutorials committee selected 6 tutorials for presentation at NAACL 2021 . the topics chosen this year range from transformers to crowdsourcing .
Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender (2022.coling-1)

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Challenge: Current modeling of 3rd person pronouns ignores neopronoun phenomena like naive pronounes, which are not (yet) widely established.
Approach: They propose to validate existing and novel approaches for modeling 3rd person pronouns in language technology and validate them through a survey.
Outcome: The proposed model excludes non-binary users, while ignoring gender-specific phenomena.
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts (2022.naacl-tutorials)

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Challenge: NAACL 2022 tutorials are delivered in a live hybrid format and also available as pre-recorded captioned videos.
Approach: NAACL 2022 tutorials are a collaboration between the conference and other conferences . they solicited tutorials on cutting-edge topics and introductory topics .
Outcome: NAACL 2022 tutorials are delivered in a live hybrid format and also available as pre-recorded captioned videos . the review committee received a total of 47 tutorial submissions, of which 6 were selected for presentation at NAAGL 2022.

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