pwc-archive/papers-with-abstracts huggingface dataset:
“Devils Are in the Details”: Annotating Specificity of Clinical Advice from Medical Literature
Authors: Yingya Li, Bei Yu
Date: 2022-07-01 00:00:00
Proceeding: NAACL (unimplicit) 2022 7
Tasks: Specificity

Prior studies have raised concerns over specificity issues in clinical advice. Lacking specificity — explicitly discussed detailed information — may affect the quality and implementation of clinical advice in medical practice. In this study, we developed and validated a fine-grained annotation schema to describe different aspects of specificity in clinical advice extracted from medical research literature. We also presented our initial annotation effort and discussed future directions towards an NLP-based specificity analysis tool for summarizing and verifying the details in clinical advice.
Analysis of Gender Bias in Social Perception and Judgement Using Chinese Word Embeddings
Authors: Jiali Li, Shucheng Zhu, Ying Liu, Pengyuan Liu
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Word Embeddings

Gender is a construction in line with social perception and judgment. An important means of this construction is through languages. When natural language processing tools, such as word embeddings, associate gender with the relevant categories of social perception and judgment, it is likely to cause bias and harm to those groups that do not conform to the mainstream social perception and judgment. Using 12,251 Chinese word embeddings as intermedium, this paper studies the relationship between social perception and judgment categories and gender. The results reveal that these grammatical gender-neutral Chinese word embeddings show a certain gender bias, which is consistent with the mainstream society’s perception and judgment of gender. Men are judged by their actions and perceived as bad, easily-disgusted, bad-tempered and rational roles while women are judged by their appearances and perceived as perfect, either happy or sad, and emotional roles.
Don’t Forget About Pronouns: Removing Gender Bias in Language Models Without Losing Factual Gender Information
Authors: Tomasz Limisiewicz, David Mareček
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Language Modeling, Language Modelling, Text Generation

The representations in large language models contain multiple types of gender information. We focus on two types of such signals in English texts: factual gender information, which is a grammatical or semantic property, and gender bias, which is the correlation between a word and specific gender. We can disentangle the model’s embeddings and identify components encoding both types of information with probing. We aim to diminish the stereotypical bias in the representations while preserving the factual gender signal. Our filtering method shows that it is possible to decrease the bias of gender-neutral profession names without significant deterioration of language modeling capabilities. The findings can be applied to language generation to mitigate reliance on stereotypes while preserving gender agreement in coreferences.
Uncertainty and Inclusivity in Gender Bias Annotation: An Annotation Taxonomy and Annotated Datasets of British English Text
Authors: Lucy Havens, Beatrice Alex, Benjamin Bach, Melissa Terras
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Language Modeling, Language Modelling

Mitigating harms from gender biased language in Natural Language Processing (NLP) systems remains a challenge, and the situated nature of language means bias is inescapable in NLP data. Though efforts to mitigate gender bias in NLP are numerous, they often vaguely define gender and bias, only consider two genders, and do not incorporate uncertainty into models. To address these limitations, in this paper we present a taxonomy of gender biased language and apply it to create annotated datasets. We created the taxonomy and annotated data with the aim of making gender bias in language transparent. If biases are communicated clearly, varieties of biased language can be better identified and measured. Our taxonomy contains eleven types of gender biases inclusive of people whose gender expressions do not fit into the binary conceptions of woman and man, and whose gender differs from that they were assigned at birth, while also allowing annotators to document unknown gender information. The taxonomy and annotated data will, in future work, underpin analysis and more equitable language model development.
Gendered Language in Resumes and its Implications for Algorithmic Bias in Hiring
Authors: Prasanna Parasurama, João Sedoc
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Fairness

Despite growing concerns around gender bias in NLP models used in algorithmic hiring, there is little empirical work studying the extent and nature of gendered language in resumes.Using a corpus of 709k resumes from IT firms, we train a series of models to classify the gender of the applicant, thereby measuring the extent of gendered information encoded in resumes.We also investigate whether it is possible to obfuscate gender from resumes by removing gender identifiers, hobbies, gender sub-space in embedding models, etc.We find that there is a significant amount of gendered information in resumes even after obfuscation.A simple Tf-Idf model can learn to classify gender with AUROC=0.75, and more sophisticated transformer-based models achieve AUROC=0.8.We further find that gender predictive values have low correlation with gender direction of embeddings – meaning that, what is predictive of gender is much more than what is “gendered” in the masculine/feminine sense.We discuss the algorithmic bias and fairness implications of these findings in the hiring context.
On the Dynamics of Gender Learning in Speech Translation
Authors: Beatrice Savoldi, Marco Gaido, Luisa Bentivogli, Matteo Negri, Marco Turchi
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Translation

Due to the complexity of bias and the opaque nature of current neural approaches, there is a rising interest in auditing language technologies. In this work, we contribute to such a line of inquiry by exploring the emergence of gender bias in Speech Translation (ST). As a new perspective, rather than focusing on the final systems only, we examine their evolution over the course of training. In this way, we are able to account for different variables related to the learning dynamics of gender translation, and investigate when and how gender divides emerge in ST. Accordingly, for three language pairs (en ? es, fr, it) we compare how ST systems behave for masculine and feminine translation at several levels of granularity. We find that masculine and feminine curves are dissimilar, with the feminine one being characterized by more erratic behaviour and late improvements over the course of training. Also, depending on the considered phenomena, their learning trends can be either antiphase or parallel. Overall, we show how such a progressive analysis can inform on the reliability and time-wise acquisition of gender, which is concealed by static evaluations and standard metrics.
Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding
Authors: Xiuying Chen, Mingzhe Li, Rui Yan, Xin Gao, Xiangliang Zhang
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Word Embeddings

Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases.However, debias on the Chinese language, one of the most spoken languages, has been less explored.Meanwhile, existing literature relies on manually created supplementary data, which is time- and energy-consuming.In this work, we propose the first Chinese Gender-neutral word Embedding model (CGE) based on Word2vec, which learns gender-neutral word embeddings without any labeled data.Concretely, CGE utilizes and emphasizes the rich feminine and masculine information contained in radicals, i.e., a kind of component in Chinese characters, during the training procedure.This consequently alleviates discriminative gender biases.Experimental results on public benchmark datasets show that our unsupervised method outperforms the state-of-the-art supervised debiased word embedding models without sacrificing the functionality of the embedding model.
An Empirical Study on the Fairness of Pre-trained Word Embeddings
Authors: Emeralda Sesari, Max Hort, Federica Sarro
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Fairness, Word Embeddings

Pre-trained word embedding models are easily distributed and applied, as they alleviate users from the effort to train models themselves. With widely distributed models, it is important to ensure that they do not exhibit undesired behaviour, such as biases against population groups. For this purpose, we carry out an empirical study on evaluating the bias of 15 publicly available, pre-trained word embeddings model based on three training algorithms (GloVe, word2vec, and fastText) with regard to four bias metrics (WEAT, SEMBIAS,DIRECT BIAS, and ECT). The choice of word embedding models and bias metrics is motivated by a literature survey over 37 publications which quantified bias on pre-trained word embeddings. Our results indicate that fastText is the least biased model (in 8 out of 12 cases) and small vector lengths lead to a higher bias.
A Taxonomy of Bias-Causing Ambiguities in Machine Translation
Authors: Michal Měchura
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Machine Translation, Translation

This paper introduces a taxonomy of phenomena which cause bias in machine translation, covering gender bias (people being male and/or female), number bias (singular you versus plural you) and formality bias (informal you versus formal you). Our taxonomy is a formalism for describing situations in machine translation when the source text leaves some of these properties unspecified (eg. does not say whether doctor is male or female) but the target language requires the property to be specified (eg. because it does not have a gender-neutral word for doctor). The formalism described here is used internally by a web-based tool we have built for detecting and correcting bias in the output of any machine translator.
On Gender Biases in Offensive Language Classification Models
Authors: Sanjana Marcé, Adam Poliak
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Classification

We explore whether neural Natural Language Processing models trained to identify offensive language in tweets contain gender biases. We add historically gendered and gender ambiguous American names to an existing offensive language evaluation set to determine whether models? predictions are sensitive or robust to gendered names. While we see some evidence that these models might be prone to biased stereotypes that men use more offensive language than women, our results indicate that these models? binary predictions might not greatly change based upon gendered names.
Gender Bias in BERT - Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task
Authors: Sophie Jentzsch, Cigdem Turan
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7

Pretrained language models are publicly available and constantly finetuned for various real-life applications. As they become capable of grasping complex contextual information, harmful biases are likely increasingly intertwined with those models. This paper analyses gender bias in BERT models with two main contributions: First, a novel bias measure is introduced, defining biases as the difference in sentiment valuation of female and male sample versions. Second, we comprehensively analyse BERT?s biases on the example of a realistic IMDB movie classifier. By systematically varying elements of the training pipeline, we can conclude regarding their impact on the final model bias. Seven different public BERT models in nine training conditions, i.e. 63 models in total, are compared. Almost all conditions yield significant gender biases. Results indicate that reflected biases stem from public BERT models rather than task-specific data, emphasising the weight of responsible usage.
Occupational Biases in Norwegian and Multilingual Language Models
Authors: Samia Touileb, Lilja Øvrelid, Erik Velldal
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Descriptive

In this paper we explore how a demographic distribution of occupations, along gender dimensions, is reflected in pre-trained language models. We give a descriptive assessment of the distribution of occupations, and investigate to what extent these are reflected in four Norwegian and two multilingual models. To this end, we introduce a set of simple bias probes, and perform five different tasks combining gendered pronouns, first names, and a set of occupations from the Norwegian statistics bureau. We show that language specific models obtain more accurate results, and are much closer to the real-world distribution of clearly gendered occupations. However, we see that none of the models have correct representations of the occupations that are demographically balanced between genders. We also discuss the importance of the training data on which the models were trained on, and argue that template-based bias probes can sometimes be fragile, and a simple alteration in a template can change a model’s behavior.
HeteroCorpus: A Corpus for Heteronormative Language Detection
Authors: Juan Vásquez, Gemma Bel-Enguix, Scott Andersen, Sergio-Luis Ojeda-Trueba
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Bias Detection, Gender Bias Detection

In recent years, plenty of work has been done by the NLP community regarding gender bias detection and mitigation in language systems. Yet, to our knowledge, no one has focused on the difficult task of heteronormative language detection and mitigation. We consider this an urgent issue, since language technologies are growing increasingly present in the world and, as it has been proven by various studies, NLP systems with biases can create real-life adverse consequences for women, gender minorities and racial minorities and queer people. For these reasons, we propose and evaluate HeteroCorpus; a corpus created specifically for studying heterononormative language in English. Additionally, we propose a baseline set of classification experiments on our corpus, in order to show the performance of our corpus in classification tasks.
Evaluating Gender Bias Transfer from Film Data
Authors: Amanda Bertsch, Ashley Oh, Sanika Natu, Swetha Gangu, Alan W. black, Emma Strubell
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Dialogue Generation, Machine Translation, Sentence, Sentence Embedding, Sentence-Embedding, Sentiment Analysis

Films are a rich source of data for natural language processing. OpenSubtitles (Lison and Tiedemann, 2016) is a popular movie script dataset, used for training models for tasks such as machine translation and dialogue generation. However, movies often contain biases that reflect society at the time, and these biases may be introduced during pre-training and influence downstream models. We perform sentiment analysis on template infilling (Kurita et al., 2019) and the Sentence Embedding Association Test (May et al., 2019) to measure how BERT-based language models change after continued pre-training on OpenSubtitles. We consider gender bias as a primary motivating case for this analysis, while also measuring other social biases such as disability. We show that sentiment analysis on template infilling is not an effective measure of bias due to the rarity of disability and gender identifying tokens in the movie dialogue. We extend our analysis to a longitudinal study of bias in film dialogue over the last 110 years and find that continued pre-training on OpenSubtitles encodes additional bias into BERT. We show that BERT learns associations that reflect the biases and representation of each film era, suggesting that additional care must be taken when using historical data.
Indigenous Language Revitalization and the Dilemma of Gender Bias
Authors: Oussama Hansal, Ngoc Tan Le, Fatiha Sadat
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Word Embeddings

Natural Language Processing (NLP), through its several applications, has been considered as one of the most valuable field in interdisciplinary researches, as well as in computer science. However, it is not without its flaws. One of the most common flaws is bias. This paper examines the main linguistic challenges of Inuktitut, an indigenous language of Canada, and focuses on gender bias identification and mitigation. We explore the unique characteristics of this language to help us understand the right techniques that can be used to identify and mitigate implicit biases. We use some methods to quantify the gender bias existing in Inuktitut word embeddings; then we proceed to mitigate the bias and evaluate the performance of the debiased embeddings. Next, we explain how approaches for detecting and reducing bias in English embeddings may be transferred to Inuktitut embeddings by properly taking into account the language’s particular characteristics. Next, we compare the effect of the debiasing techniques on Inuktitut and English. Finally, we highlight some future research directions which will further help to push the boundaries.
Why Knowledge Distillation Amplifies Gender Bias and How to Mitigate from the Perspective of DistilBERT
Authors: Jaimeen Ahn, Hwaran Lee, JinHwa Kim, Alice Oh
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Knowledge Distillation

Knowledge distillation is widely used to transfer the language understanding of a large model to a smaller model.However, after knowledge distillation, it was found that the smaller model is more biased by gender compared to the source large model.This paper studies what causes gender bias to increase after the knowledge distillation process.Moreover, we suggest applying a variant of the mixup on knowledge distillation, which is used to increase generalizability during the distillation process, not for augmentation.By doing so, we can significantly reduce the gender bias amplification after knowledge distillation.We also conduct an experiment on the GLUE benchmark to demonstrate that even if the mixup is applied, it does not have a significant adverse effect on the model’s performance.
Incorporating Subjectivity into Gendered Ambiguous Pronoun (GAP) Resolution using Style Transfer
Authors: Kartikey Pant, Tanvi Dadu
Date: 2022-07-01 00:00:00
Proceeding: NAACL (GeBNLP) 2022 7
Tasks: Bias Detection, coreference-resolution, Coreference Resolution, Gender Bias Detection, Style Transfer

The GAP dataset is a Wikipedia-based evaluation dataset for gender bias detection in coreference resolution, containing mostly objective sentences. Since subjectivity is ubiquitous in our daily texts, it becomes necessary to evaluate models for both subjective and objective instances. In this work, we present a new evaluation dataset for gender bias in coreference resolution, GAP-Subjective, which increases the coverage of the original GAP dataset by including subjective sentences. We outline the methodology used to create this dataset. Firstly, we detect objective sentences and transfer them into their subjective variants using a sequence-to-sequence model. Secondly, we outline the thresholding techniques based on fluency and content preservation to maintain the quality of the sentences. Thirdly, we perform automated and human-based analysis of the style transfer and infer that the transferred sentences are of high quality. Finally, we benchmark both GAP and GAP-Subjective datasets using a BERT-based model and analyze its predictive performance and gender bias.
Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
Authors: Mihai Surdeanu, John Hungerford, Yee Seng Chan, Jessica MacBride, Benjamin Gyori, Andrew Zupon, Zheng Tang, Haoling Qiu, Bonan Min, Yan Zverev, Caitlin Hilverman, Max Thomas, Walter Andrews, Keith Alcock, Zeyu Zhang, Michael Reynolds, Steven Bethard, Rebecca Sharp, Egoitz Laparra
Date: 2022-07-01 00:00:00
Proceeding: NAACL (HCINLP) 2022 7
Tasks: Text Summarization

An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none.When one does exist, as the information needs shift, it must be continually extended. This is a slow and tedious task, and one which does not scale well.Here we propose an interactive tool that allows a taxonomy to be built or extended rapidly and with a human in the loop to control precision. We apply insights from text summarization and information extraction to reduce the search space dramatically, then leverage modern pretrained language models to perform contextualized clustering of the remaining concepts to yield candidate nodes for the user to review. We show this allows a user to consider as many as 200 taxonomy concept candidates an hour, to quickly build or extend a taxonomy to better fit information needs.
An Interactive Exploratory Tool for the Task of Hate Speech Detection
Authors: Angelina McMillan-Major, Amandalynne Paullada, Yacine Jernite
Date: 2022-07-01 00:00:00
Proceeding: NAACL (HCINLP) 2022 7
Tasks: Hate Speech Detection

With the growth of Automatic Content Moderation (ACM) on widely used social media platforms, transparency into the design of moderation technology and policy is necessary for online communities to advocate for themselves when harms occur.In this work, we describe a suite of interactive modules to support the exploration of various aspects of this technology, and particularly of those components that rely on English models and datasets for hate speech detection, a subtask within ACM. We intend for this demo to support the various stakeholders of ACM in investigating the definitions and decisions that underpin current technologies such that those with technical knowledge and those with contextual knowledge may both better understand existing systems.
Design Considerations for an NLP-Driven Empathy and Emotion Interface for Clinician Training via Telemedicine
Authors: Roxana Girju, Marina Girju
Date: 2022-07-01 00:00:00
Proceeding: NAACL (HCINLP) 2022 7

As digital social platforms and mobile technologies become more prevalent and robust, the use of Artificial Intelligence (AI) in facilitating human communication will grow. This, in turn, will encourage development of intuitive, adaptive, and effective empathic AI interfaces that better address the needs of socially and culturally diverse communities. In this paper, we present several design considerations of an intelligent digital interface intended to guide the clinicians toward more empathetic communication. This approach allows various communities of practice to investigate how AI, on one side, and human communication and healthcare needs, on the other, can contribute to each other’s development.