GenAI4ELab/papercli-papers huggingface dataset:
Comparing Kaldi-Based Pipeline Elpis and Whisper for Čakavian Transcription
Authors: Austin Jones; Shulin Zhang; John Hale; Margaret Renwick; Zvjezdana Vrzic; Keith Langston
Venue: ACL 2024 (Proceedings of the Third Workshop on NLP Applications to Field Linguistics)

Abstract: Automatic speech recognition (ASR) has the potential to accelerate the documentation of endangered languages, but the dearth of resources poses a major obstacle. Čakavian, an endangered variety spoken primarily in Croatia, is a case in point, lacking transcription tools that could aid documentation efforts. We compare training a new ASR model on a limited dataset using the Kaldi-based ASR pipeline Elpis to using the same dataset to adapt the transformer-based pretrained multilingual model Whisper, to determine which is more practical in the documentation context. Results show that Whisper outperformed Elpis, achieving the lowest average Word Error Rate (WER) of 57.3% and median WER of 35.48%. While Elpis offers a less computationally expensive model and friendlier user experience, Whisper appears better at adapting to our collected Čakavian data.
Zero-shot Cross-lingual POS Tagging for Filipino
Authors: Jimson Paulo Layacan; Isaiah Edri W. Flores; Katrina Bernice M. Tan; Ma. Regina E. Estuar; Jann Railey E. Montalan; Marlene M. De Leon
Venue: ACL 2024 (Proceedings of the Third Workshop on NLP Applications to Field Linguistics)

Abstract: Supervised learning approaches in NLP, exemplified by POS tagging, rely heavily on the presence of large amounts of annotated data. However, acquiring such data often requires significant amount of resources and incurs high costs. In this work, we explore zero-shot cross-lingual transfer learning to address data scarcity issues in Filipino POS tagging, particularly focusing on optimizing source language selection. Our zero-shot approach demonstrates superior performance compared to previous studies, with top-performing fine-tuned PLMs achieving F1 scores as high as 79.10%. The analysis reveals moderate correlations between cross-lingual transfer performance and specific linguistic distances–featural, inventory, and syntactic–suggesting that source languages with these features closer to Filipino provide better results. We identify tokenizer optimization as a key challenge, as PLM tokenization sometimes fails to align with meaningful representations, thus hindering POS tagging performance.
A Parameter-Efficient Multi-Objective Approach to Mitigate Stereotypical Bias in Language Models
Authors: Yifan Wang; Vera Demberg
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Pre-trained language models have shown impressive abilities of understanding and generating natural languages. However, they typically inherit undesired human-like bias and stereotypes from training data, which raises concerns about putting these models into use in real-world scenarios. Although prior research has proposed to reduce bias using different fairness objectives, they usually fail to capture different representations of bias and, therefore, struggle with fully debiasing models. In this work, we introduce a multi-objective probability alignment approach to overcome current challenges by incorporating multiple debiasing losses to locate and penalize bias in different forms. Compared to existing methods, our proposed method can more effectively and comprehensively reduce stereotypical bias, and maintains the language ability of pre-trained models at the same time. Besides, we adopt prefix-tuning to optimize fairness objectives, and results show that it can achieve better bias removal than full fine-tuning while requiring much fewer computational resources. Our code and data are available at https://github.com/Ewanwong/debias_NLG.
Do PLMs and Annotators Share the Same Gender Bias? Definition, Dataset, and Framework of Contextualized Gender Bias
Authors: Shucheng Zhu; Bingjie Du; Jishun Zhao; Ying Liu; Pengyuan Liu
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Pre-trained language models (PLMs) have achieved success in various of natural language processing (NLP) tasks. However, PLMs also introduce some disquieting safety problems, such as gender bias. Gender bias is an extremely complex issue, because different individuals may hold disparate opinions on whether the same sentence expresses harmful bias, especially those seemingly neutral or positive. This paper first defines the concept of contextualized gender bias (CGB), which makes it easy to measure implicit gender bias in both PLMs and annotators. We then construct CGBDataset, which contains 20k natural sentences with gendered words, from Chinese news. Similar to the task of masked language models, gendered words are masked for PLMs and annotators to judge whether a male word or a female word is more suitable. Then, we introduce CGBFrame to measure the gender bias of annotators. By comparing the results measured by PLMs and annotators, we find that though there are differences on the choices made by PLMs and annotators, they show significant consistency in general.
We Don’t Talk About That: Case Studies on Intersectional Analysis of Social Bias in Large Language Models
Authors: Hannah Devinney; Jenny Björklund; Henrik Björklund
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Despite concerns that Large Language Models (LLMs) are vectors for reproducing and amplifying social biases such as sexism, transphobia, islamophobia, and racism, there is a lack of work qualitatively analyzing how such patterns of bias are generated by LLMs. We use mixed-methods approaches and apply a feminist, intersectional lens to the problem across two language domains, Swedish and English, by generating narrative texts using LLMs. We find that hegemonic norms are consistently reproduced; dominant identities are often treated as ‘default’; and discussion of identity itself may be considered ‘inappropriate’ by the safety features applied to some LLMs. Due to the differing behaviors of models, depending both on their design and the language they are trained on, we observe that strategies of identifying “bias” must be adapted to individual models and their socio-cultural contexts._Content warning: This research concerns the identification of harms, including stereotyping, denigration, and erasure of minoritized groups. Examples, including transphobic and racist content, are included and discussed._
An Explainable Approach to Understanding Gender Stereotype Text
Authors: Manuela Jeyaraj; Sarah Delany
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Gender Stereotypes refer to the widely held beliefs and assumptions about the typical traits, behaviours, and roles associated with a collective group of individuals of a particular gender in society. These typical beliefs about how people of a particular gender are described in text can cause harmful effects to individuals leading to unfair treatment. In this research, the aim is to identify the words and language constructs that can influence a text to be considered a gender stereotype. To do so, a transformer model with attention is fine-tuned for gender stereotype detection. Thereafter, words/language constructs used for the model’s decision are identified using a combined use of attention- and SHAP (SHapley Additive exPlanations)-based explainable approaches. Results show that adjectives and verbs were highly influential in predicting gender stereotypes. Furthermore, applying sentiment analysis showed that words describing male gender stereotypes were more positive than those used for female gender stereotypes.
A Fairness Analysis of Human and AI-Generated Student Reflection Summaries
Authors: Bhiman Kumar Baghel; Arun Balajiee Lekshmi Narayanan; Michael Miller Yoder
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: This study examines the fairness of human- and AI-generated summaries of student reflections in university STEM classes, focusing on potential gender biases. Using topic modeling, we first identify topics that are more prevalent in reflections from female students and others that are more common among male students. We then analyze whether human and AI-generated summaries reflect the concerns of students of any particular gender over others. Our analysis reveals that though human-generated and extractive AI summarization techniques do not show a clear bias, abstractive AI-generated summaries exhibit a bias towards male students. Pedagogical themes are over-represented from male reflections in these summaries, while concept-specific topics are under-represented from female reflections. This research contributes to a deeper understanding of AI-generated bias in educational contexts, highlighting the need for future work on mitigating these biases.
On Shortcuts and Biases: How Finetuned Language Models Distinguish Audience-Specific Instructions in Italian and English
Authors: Nicola Fanton; Michael Roth
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Instructional texts for different audience groups can help to address specific needs, but at the same time run the risk of perpetrating biases. In this paper, we extend previous findings on disparate social norms and subtle stereotypes in wikiHow in two directions: We explore the use of fine-tuned language models to determine how audience-specific instructional texts can be distinguished and we transfer the methodology to another language, Italian, to identify cross-linguistic patterns. We find that language models mostly rely on group terms, gender markings, and attributes reinforcing stereotypes.
The power of Prompts: Evaluating and Mitigating Gender Bias in MT with LLMs
Authors: Aleix Sant; Carlos Escolano; Audrey Mash; Francesca De Luca Fornaciari; Maite Melero
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: This paper studies gender bias in machine translation through the lens of Large Language Models (LLMs). Four widely-used test sets are employed to benchmark various base LLMs, comparing their translation quality and gender bias against state-of-the-art Neural Machine Translation (NMT) models for English to Catalan (En → Ca) and English to Spanish (En → Es) translation directions. Our findings reveal pervasive gender bias across all models, with base LLMs exhibiting a higher degree of bias compared to NMT models. To combat this bias, we explore prompting engineering techniques applied to an instruction-tuned LLM. We identify a prompt structure that significantly reduces gender bias by up to 12% on the WinoMT evaluation dataset compared to more straightforward prompts. These results significantly reduce the gender bias accuracy gap between LLMs and traditional NMT systems.
Detecting Gender Discrimination on Actor Level Using Linguistic Discourse Analysis
Authors: Stefanie Urchs; Veronika Thurner; Matthias Aßenmacher; Christian Heumann; Stephanie Thiemichen
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: With the usage of tremendous amounts of text data for training powerful large language models such as ChatGPT, the issue of analysing and securing data quality has become more pressing than ever. Any biases, stereotypes and discriminatory patterns that exist in the training data can be reproduced, reinforced or broadly disseminated by the models in production. Therefore, it is crucial to carefully select and monitor the text data that is used as input to train the model. Due to the vast amount of training data, this process needs to be (at least partially) automated. In this work, we introduce a novel approach for automatically detecting gender discrimination in text data on the actor level based on linguistic discourse analysis. Specifically, we combine existing information extraction (IE) techniques to partly automate the qualitative research done in linguistic discourse analysis. We focus on two important steps: Identifying the respectiveperson-named-entity (an actor) and all forms it is referred to (Nomination), and detecting the characteristics it is ascribed (Predication). Asa proof of concept, we integrate these two steps into a pipeline for automated text analysis. The separate building blocks of the pipeline could be flexibly adapted, extended, and scaled for bigger datasets to accommodate a wide range of usage scenarios and specific ML tasks or help social scientists with analysis tasks. We showcase and evaluate our approach on several real and simulated exemplary texts.
What Can Go Wrong in Authorship Profiling: Cross-Domain Analysis of Gender and Age Prediction
Authors: Hongyu Chen; Michael Roth; Agnieszka Falenska
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Authorship Profiling (AP) aims to predict the demographic attributes (such as gender and age) of authors based on their writing styles. Ever-improving models mean that this task is gaining interest and application possibilities. However, with greater use also comes the risk that authors are misclassified more frequently, and it remains unclear to what extent the better models can capture the bias and who is affected by the models’ mistakes. In this paper, we investigate three established datasets for AP as well as classical and neural classifiers for this task. Our analyses show that it is often possible to predict the demographic information of the authors based on textual features. However, some features learned by the models are specific to datasets. Moreover, models are prone to errors based on stereotypes associated with topical bias.
Towards Fairer NLP Models: Handling Gender Bias In Classification Tasks
Authors: Nasim Sobhani; Sarah Delany
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Measuring and mitigating gender bias in natural language processing (NLP) systems is crucial to ensure fair and ethical AI. However, a key challenge is the lack of explicit gender information in many textual datasets. This paper proposes two techniques, Identity Term Sampling (ITS) and Identity Term Pattern Extraction (ITPE), as alternatives to template-based approaches for measuring gender bias in text data. These approaches identify test data for measuring gender bias in the dataset itself and can be used to measure gender bias on any NLP classifier. We demonstrate the use of these approaches for measuring gender bias across various NLP classification tasks, including hate speech detection, fake news identification, and sentiment analysis. Additionally, we show how these techniques can benefit gender bias mitigation, proposing a variant of Counterfactual Data Augmentation (CDA), called Gender-Selective CDA (GS-CDA), which reduces the amount of data augmentation required in training data while effectively mitigating gender bias and maintaining overall classification performance.
Investigating Gender Bias in STEM Job Advertisements
Authors: Malika Dikshit; Houda Bouamor; Nizar Habash
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Gender inequality has been historically prevalent in academia, especially within the fields of Science, Technology, Engineering, and Mathematics (STEM). In this study, we propose to examine gender bias in academic job descriptions in the STEM fields. We go a step further than previous studies that merely identify individual words as masculine-coded and feminine-coded and delve into the contextual language used in academic job advertisements. We design a novel approach to detect gender biases in job descriptions using Natural Language Processing techniques. Going beyond binary masculine-feminine stereotypes, we propose three big group types to understand gender bias in the language of job descriptions, namely agentic, balanced, and communal. We cluster similar information in job descriptions into these three groups using contrastive learning and various clustering techniques. This research contributes to the field of gender bias detection by providing a novel approach and methodology for categorizing gender bias in job descriptions, which can aid more effective and targeted job advertisements that will be equally appealing across all genders.
Dissecting Biases in Relation Extraction: A Cross-Dataset Analysis on People’s Gender and Origin
Authors: Marco Stranisci; Pere-Lluís Huguet Cabot; Elisa Bassignana; Roberto Navigli
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: 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.
Gender Bias in Turkish Word Embeddings: A Comprehensive Study of Syntax, Semantics and Morphology Across Domains
Authors: Duygu Altinok
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: 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.
Disagreeable, Slovenly, Honest and Un-named Women? Investigating Gender Bias in English Educational Resources by Extending Existing Gender Bias Taxonomies
Authors: Haotian Zhu; Kexin Gao; Fei Xia; Mari Ostendorf
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Gender bias has been extensively studied in both the educational field and the Natural Language Processing (NLP) field, the former using human coding to identify patterns associated with and causes of gender bias in text and the latter to detect, measure and mitigate gender bias in NLP output and models. This work aims to use NLP to facilitate automatic, quantitative analysis of educational text within the framework of a gender bias taxonomy. Analyses of both educational texts and a lexical resource (WordNet) reveal patterns of bias that can inform and aid educators in updating textbooks and lexical resources and in designing assessment items.
Generating Gender Alternatives in Machine Translation
Authors: Sarthak Garg; Mozhdeh Gheini; Clara Emmanuel; Tatiana Likhomanenko; Qin Gao; Matthias Paulik
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Machine translation (MT) systems often translate terms with ambiguous gender (e.g., English term “the nurse”) into the gendered form that is most prevalent in the systems’ training data (e.g., “enfermera”, the Spanish term for a female nurse). This often reflects and perpetuates harmful stereotypes present in society. With MT user interfaces in mind that allow for resolving gender ambiguity in a frictionless manner, we study the problem of generating all grammatically correct gendered translation alternatives. We open source train and test datasets for five language pairs and establish benchmarks for this task. Our key technical contribution is a novel semi-supervised solution for generating alternatives that integrates seamlessly with standard MT models and maintains high performance without requiring additional components or increasing inference overhead.
Beyond Binary Gender Labels: Revealing Gender Bias in LLMs through Gender-Neutral Name Predictions
Authors: Zhiwen You; HaeJin Lee; Shubhanshu Mishra; Sullam Jeoung; Apratim Mishra; Jinseok Kim; Jana Diesner
Venue: ACL 2024 (Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP))

Abstract: Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can be problematic in the cases of gender-neutral names that do not align with any one gender, among other reasons. Relying solely on binary gender categories without recognizing gender-neutral names can reduce the inclusiveness of gender prediction tasks. We introduce an additional gender category, i.e., “neutral”, to study and address potential gender biases in Large Language Models (LLMs). We evaluate the performance of several foundational and large language models in predicting gender based on first names only. Additionally, we investigate the impact of adding birth years to enhance the accuracy of gender prediction, accounting for shifting associations between names and genders over time. Our findings indicate that most LLMs identify male and female names with high accuracy (over 80%) but struggle with gender-neutral names (under 40%), and the accuracy of gender prediction is higher for English-based first names than non-English names. The experimental results show that incorporating the birth year does not improve the overall accuracy of gender prediction, especially for names with evolving gender associations. We recommend using caution when applying LLMs for gender identification in downstream tasks, particularly when dealing with non-binary gender labels.