Women’s Syntactic Resilience and Men’s Grammatical Luck: Gender-Bias in Part-of-Speech Tagging and Dependency Parsing (P19-1)
Copied to clipboard
| Challenge: | linguistic studies have shown the prevalence of various lexical and grammatical patterns in texts authored by a person of a particular gender, but models for part-of-speech tagging and dependency parsing have not adapted to account for these differences. |
| Approach: | They annotate the Wall Street Journal part of the Penn Treebank with the gender information of the articles’ authors and build taggers and parsers trained on this data. |
| Outcome: | The proposed model can account for gendered differences in syntactic tasks and highlight future venues for developing more accurate taggers and parsers. |
Similar Papers
Under the Morphosyntactic Lens: A Multifaceted Evaluation of Gender Bias in Speech Translation (2022.acl-long)
Copied to clipboard
| 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. |
| Outcome: | The proposed models shed light on gender bias and its detection at several levels of granularity. |
Identifying and Reducing Gender Bias in Word-Level Language Models (N19-3)
Copied to clipboard
| 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. |
| Outcome: | The proposed method reduces gender bias up to an optimal weight assigned to the loss term, and the model becomes unstable as the perplexity increases. |
Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus (2020.acl-main)
Copied to clipboard
| Challenge: | a growing number of studies have examined the issue of gender bias in speech translation . a gender bias is a systemic problem that reproduces gender stereotypes discriminating women. |
| Approach: | They present the first thorough investigation of gender bias in speech translation . they compare audio technologies for English-Italian/French translations . |
| Outcome: | The proposed method compares different technologies on two languages, English and French. |
Robustness and Reliability of Gender Bias Assessment in Word Embeddings: The Role of Base Pairs (2020.aacl-main)
Copied to clipboard
| Challenge: | Existing methods to quantify gender bias in word embeddings are not robust and cannot identify common types of bias. |
| Approach: | They propose to quantify gender bias by using cosine similarity to a pair of gender words and using analogies. |
| Outcome: | The proposed methods are not robust and cannot identify common types of bias, while analogies are unsuitable indicators. |
Examining Gender Bias in Languages with Grammatical Gender (D19-1)
Copied to clipboard
| Challenge: | Existing studies on gender bias in word embeddings focus on English . however, these studies cannot be extended to languages with morphological agreement on gender . |
| Approach: | They propose new metrics to evaluate gender bias in word embeddings of English and Spanish . they extend existing approaches to mitigate gender bias while preserving original embeddables . |
| Outcome: | The proposed methods reduce gender bias while preserving the original embeddings. |
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
Copied to clipboard
Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
| Approach: | They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
Different Speech Translation Models Encode and Translate Speaker Gender Differently (2025.acl-short)
Copied to clipboard
| Challenge: | Recent studies on interpreting the hidden states of speech models have shown their ability to capture speaker-specific features, including gender. |
| Approach: | They propose to use probing methods to assess gender encoding across ST models. |
| Outcome: | The proposed models capture speaker-specific features, including gender, while older models do not . low gender encoding capabilities result in systems’ tendency toward a masculine default, a translation bias that is more pronounced in newer architectures. |
On Evaluating and Mitigating Gender Biases in Multilingual Settings (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing benchmarks and resources for evaluating gender biases in multilingual settings are limited. |
| Approach: | They propose to extend DisCo to different Indian languages using human annotations to evaluate gender biases in multilingual models. |
| Outcome: | The proposed benchmarks and mitigation techniques are extended beyond English to evaluate gender biases in multilingual models. |
Automatically Inferring Gender Associations from Language (D19-1)
Copied to clipboard
| Challenge: | In this paper, we demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. |
| Approach: | They propose to integrate two datasets and a novel approach to automatically infer gender associations from language and find coherent word clusters and label clusters for the semantic concepts they represent. |
| Outcome: | The proposed methods outperform strong baselines in large-scale studies of how people talk about women and men in two different settings. |
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation (2021.findings-emnlp)
Copied to clipboard
| 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. |