Thailand

Relation Extraction (RE) is at the core of many Natural Language Understanding tasks, including knowledge-base population and Question Answering. However, any Natural Language Processing system is exposed to biases, and the analysis of these has not received much attention in RE. We propose a new method for inspecting bias in the RE pipeline, which is completely transparent in terms of interpretability. Specifically, in this work we analyze biases related to gender and place of birth. Our methodology includes (i) obtaining semantic triplets (subject, object, semantic relation) involving &#39;person&#39; entities from RE resources, (ii) collecting meta-information (&#39;gender&#39; and &#39;place of birth&#39;) using Entity Linking technologies, and then (iii) analyze the distribution of triplets across different groups (e.g., men versus women). We investigate bias at two levels: In the training data of three commonly used RE datasets (SREDFM, CrossRE, NYT), and in the predictions of a state-of-the-art RE approach (ReLiK). To enable cross-dataset analysis, we introduce a taxonomy of relation types mapping the label sets of different RE datasets to a unified label space. Our findings reveal that bias is a compounded issue affecting underrepresented groups within data and predictions for RE.

ACL 2024

Dissecting Biases in Relation Extraction: A Cross-Dataset Analysis on People&#39;s Gender and Origin

socio-demographic biases

relation extraction

bias

information extraction

Relation Extraction (RE) is at the core of many Natural Language Understanding tasks, including knowledge-base population and Question Answering. However, any Natural Language Processing system is exposed to biases, and the analysis of these has not received much attention in RE. We propose a new method for inspecting bias in the RE pipeline, which is completely transparent in terms of interpretability. Specifically, in this work we analyze biases related to gender and place of birth. Our methodology includes (i) obtaining semantic triplets (subject, object, semantic relation) involving 'person' entities from RE resources, (ii) collecting meta-information ('gender' and 'place of birth') using Entity Linking technologies, and then (iii) analyze the distribution of triplets across different groups (e.g., men versus women). We investigate bias at two levels: In the training data of three commonly used RE datasets (SREDFM, CrossRE, NYT), and in the predictions of a state-of-the-art RE approach (ReLiK). To enable cross-dataset analysis, we introduce a taxonomy of relation types mapping the label sets of different RE datasets to a unified label space. Our findings reveal that bias is a compounded issue affecting underrepresented groups within data and predictions for RE.

Dissecting Biases in Relation Extraction: A Cross-Dataset Analysis on People's Gender and Origin

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Gender bias in word representations has emerged as a prominent research area in recent years. While numerous studies have focused on measuring and addressing bias in English word embeddings, research on the Turkish language remains limited. This work aims to bridge this gap by conducting a comprehensive evaluation of gender bias in Turkish word embeddings, considering the dimensions of syntax, semantics, and morphology. We employ subword-based static word vectors trained on three distinct domains: web crawl, academical text, and medical text. Through the analysis of gender-associated words in each domain, we not only uncover gender bias but also gain insights into the unique characteristics of these domains. Additionally, we explore the influence of Turkish suffixes on word gender, providing a novel perspective on gender bias. Our findings reveal the pervasive nature of gender biases across various aspects of the Turkish language, including word frequency, semantics, parts-of-speech, and even the smallest linguistic unit - suffixes. Notably, we demonstrate that the majority of noun and verb lemmas, as well as adverbs and adjectives, exhibit masculine gendering in the general-purpose written language. This study is the first of its kind to offer a comprehensive examination of gender bias in the Turkish language.

Gender Bias in Turkish Word Embeddings: A Comprehensive Study of Syntax, Semantics and Morphology Across Domains

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Disagreeable, Slovenly, Honest and Un-named Women? Investigating Gender Bias in English Educational Resources by Extending Existing Gender Bias Taxonomies

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Beyond Binary Gender Labels: Revealing Gender Bias in LLMs through Gender-Neutral Name Predictions

In this paper, we revisit the seminal work of Garimella et al. 2019, who reported that dependency parsers learn demographically-related signals from their training data and perform differently on sentences authored by people of different genders. We re-run all the parsing experiments from Garimella et al. 2019 and find that their results are not reproducible. Additionally, the original patterns suggesting the presence of gender biases fail to generalize to other treebank and parsing architecture. Instead, our data analysis uncovers methodological shortcomings in the initial study that artificially introduced differences into female and male datasets during preprocessing. These disparities potentially compromised the validity of the original conclusions.

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Fine-tuning three different LLMs with this dataset, we observe an overall reduction in gender-stereotyping tendencies across the models. Our approach provides a practical method for enhancing gender inclusivity in LLM training data and contributes to incorporating queer-feminist linguistic activism in bias mitigation research in NLP.

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Stop! In the Name of Flaws: Disentangling Personal Names and Sociodemographic Attributes in NLP

We evaluate gender biases in multilingual multimodal image and text models in two settings: text-to-image retrieval and text-to-image generation, to show that even seemingly gender-neutral traits generate biased results. We evaluate our framework in the context of people from India, working with two languages: English and Hindi. We work with frameworks built around mCLIP-based models to ensure a thorough evaluation of recent state-of-the-art models in the multilingual setting due to their potential for widespread applications. We analyze the results across 50 traits for retrieval and 8 traits for generation, showing that current multilingual multimodal models are biased towards men for most traits, and this problem is further exacerbated for lower-resource languages like Hindi. We further discuss potential reasons behind this observation, particularly stemming from the bias introduced by the pretraining datasets.

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This study explores the effect of annotators' demographic features on labeling sexist content in social media datasets, specifically focusing on the EXIST dataset, which includes direct sexist messages, reports and descriptions of sexist experiences and stereotypes. We investigate how various demographic backgrounds influence annotation outcomes and examine methods to incorporate these features into BERT-based model training. Our experiments demonstrate that adding demographic information improves performance in detecting sexism and assessing intention of the author.

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Dissecting Biases in Relation Extraction: A Cross-Dataset Analysis on People's Gender and Origin

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Gender Bias in Turkish Word Embeddings: A Comprehensive Study of Syntax, Semantics and Morphology Across Domains

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