| 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. |
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Gender Bias in Coreference Resolution (N18-2)
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| 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. |
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. |
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 . |
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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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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. |
| Approach: | They propose a trick to improve resolution by antecedent switching to target common-sense understanding and world knowledge. |
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WikiCREM: A Large Unsupervised Corpus for Coreference Resolution (D19-1)
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| Challenge: | Large-scale training sets for pronoun resolution are scarce, since manually labelling data is costly. |
| Approach: | They propose a language-model-based approach to solve pronoun disambiguation problems using a WikiCREM dataset. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on 6 out of 7 datasets. |
Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation (2020.acl-main)
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| Challenge: | Existing methods to debias word embeddings from human-generated corpora inherit strong gender bias . prior work has suggested removing gender component from pre-trained word embeds or compressing gender information into a few dimensions of the embeddable space . |
| Approach: | They propose a technique that purifies word embeddings against inferred gender subspaces . they propose to preserve distributional semantics of pre-trained word embeds while reducing gender bias . |
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Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)
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| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
| Approach: | They propose a metric to measure gender bias and a regularization loss term to minimize embeddings onto an embeddable subspace that encodes gender. |
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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. |
| Outcome: | The proposed method is based on two recent coreference resolution datasets composed of English sentences cast participants into non-stereotypical gender roles. |
Evaluating Gender Bias in Speech Translation (2022.lrec-1)
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| Challenge: | Existing evaluation techniques for gender biases are lacking in the field of machine translation. |
| Approach: | They propose to use a free evaluation set to evaluate gender bias in speech translation. |
| Outcome: | The proposed set is the speech version of WinoMT, an MT challenge set. |