| Challenge: | a study of coreference resolution systems that resolve gender differences in pairs is aimed at examining implicit gender biases. |
| Approach: | They propose a Winograd schema-style set of minimal pair sentences that differ only by gender . they evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems . |
| Outcome: | The proposed system resolves a male and neutral pronoun as coreferent with "The surgeon" but does not resolve the female pronounce. |
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Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods (N18-2)
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| Challenge: | Existing methods for co-reference resolution focus on gender bias. |
| Approach: | They propose a new benchmark for co-reference resolution focused on gender bias, WinoBias. |
| Outcome: | The proposed system removes the bias without significantly affecting performance on existing datasets. |
Toward Gender-Inclusive Coreference Resolution (2020.acl-main)
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| Challenge: | a recent study shows that coreference resolution systems can be harmful to binary and non-binary trans and cis stakeholders. |
| Approach: | They propose to use gender-based crowd annotations to investigate coreference resolution biases . they use a dataset to examine the complexity of gender in crowd annotation systems . |
| Outcome: | a new study shows that without acknowledging and building systems that recognize gender, we build systems that lead to many potential harms. |
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (2021.findings-emnlp)
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| Challenge: | Recent studies have found evidence of gender bias in machine translation and coreference resolution models using mostly synthetic diagnostic datasets. |
| Approach: | They propose a semi-automatic method to vastly extend synthetic, small diagnostic datasets to include grammatical patterns indicating stereotypical and non-stereotypical gender-role assignments. |
| Outcome: | The proposed method extends the existing dataset to 108K diverse English sentences. |
Gender Bias in Contextualized Word Embeddings (N19-1)
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| Challenge: | Existing studies show that training word embeddings in large corpora could lead to encoding societal biases present in these human-produced data. |
| Approach: | They conduct several intrinsic analyses to quantify, analyze and mitigate gender bias exhibited in ELMo’s contextualized word vectors. |
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GKnow: Measuring the Entanglement of Gender Bias and Factual Gender (2026.acl-long)
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| Challenge: | Recent studies have focused on mitigating gender bias, but mechanistic interpretations of gender fail to distinguish between factually gendered outputs and gender biased outputs. |
| Approach: | They propose a benchmark to assess gender knowledge and gender bias in language models . they use neuron ablation to disentangle stereotypical and factual gender . |
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Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation (2022.acl-long)
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| Challenge: | grammatical gender languages are characterized by morphosyntactic chains of gender agreement marked on a variety of lexical items and parts-of-speech (POS). |
| Approach: | They propose to enrich the natural, gender-sensitive MuST-SHE corpus with two new linguistic annotation layers to explore gender bias. |
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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". |
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Evaluating Gender Bias in Machine Translation (P19-1)
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| Challenge: | Using morphological analysis, we find that MT models exhibit gender-biased translation errors when training data encode stereotypes not relevant for the task. |
| Approach: | They propose an automatic gender bias evaluation method for eight target languages with grammatical gender based on morphological analysis. |
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The KnowRef Coreference Corpus: Removing Gender and Number Cues for Difficult Pronominal Anaphora Resolution (P19-1)
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| Challenge: | Existing methods for coreference resolution exploit the number and gender of antecedents or have been handcrafted and do not reflect the diversity of naturally occurring text. |
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Beyond Names: How Grammatical Gender Markers Bias LLM-based Educational Recommendations (2026.eacl-long)
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| Challenge: | grammatical gender cues alone trigger substantial distributional shifts in educational recommendations . authors show that up to 76% of the bias exhibited when using prompts with proper names is already present with grammatical gender markers alone. |
| Approach: | They investigate gender biases exhibited by LLM-based virtual assistants in Italian . they show that simply changing noun and adjective endings significantly shifts recommendations . |
| Outcome: | The findings highlight the need for robust bias evaluation and mitigation strategies before deploying LLM-based virtual assistants in student-facing contexts. |