Evaluating the Underlying Gender Bias in Contextualized Word Embeddings

Gender bias is highly impacting natural language processing applications. Word embeddings have clearly been proven both to keep and amplify gender biases that are present in current data sources. Recently, contextualized word embeddings have enhanced previous word embedding techniques by computing word vector representations dependent on the sentence they appear in. In this paper, we study the impact of this conceptual change in the word embedding computation in relation with gender bias. Our analysis includes different measures previously applied in the literature to standard word embeddings. Our findings suggest that contextualized word embeddings are less biased than standard ones even when the latter are debiased

Gender bias in natural language processing

(English) Gender bias is a dangerous form of social bias impacting an essential group of people. The effect of gender bias is propagated to our data, causing the accuracy of the predictions in models to be different depending on gender. In the deep learning era, our models are highly impacted by the training data transferring the negative biases in the data to the models. Natural Language Processing models encounter this amplification of bias in the data. Our thesis is devoted to studying the issue of gender bias in NLP applications from different points of view. To understand and manage the effect of bias amplification, evaluation and mitigation approaches have to be explored. The scientific society has exerted significant efforts in these two directions to enable proposing solutions to the problem. Our thesis is devoted to these two main directions; proposing evaluation schemes, whether as datasets or mechanisms, besides suggesting mitigation techniques. For evaluation, we proposed techniques for evaluating bias in contextualized embeddings and multilingual translation models. Besides, we presented benchmarks for evaluating bias for speech translation and multilingual machine translation models. For mitigation direction, we proposed different approaches in machine translation models by adding contextual text, contextual embeddings, or relaxing the architecture’s constraints. Our evaluation studies concluded that gender bias is encoded strongly in contextual embeddings representing professions and stereotypical nouns. We also unveiled that algorithms amplify the bias and that the system’s architecture impacts the behavior. For the evaluation purposes, we contributed to creating several benchmarks for the evaluation purpose; we introduced a benchmark that evaluates gender bias in speech translation systems. This research suggests that the current state of speech translation systems does not enable us to evaluate gender bias accurately because of the low quality of speech translation systems. Additionally, we proposed a toolkit for building multilingual balanced datasets for training and evaluating NMT models. These datasets are balanced within the gender occupation-wise. We found out that high-resource languages usually tend to predict more precise male translations. Our mitigation studies in NMT suggest that the nature of datasets and languages needs to be considered to apply the right approach. Mitigating bias can rely on adding contextual information. However, in other cases, we need to rethink the model and relax some influencing conditions to the bias that do not affect the general performance but reduce the effect of bias amplification.(Español) El prejuicio de género es una forma peligrosa de sesgo social que afecta a un grupo esencial de personas. El efecto del prejuicio de género se propaga a nuestros datos, lo que hace quela precisión de las predicciones en los modelos sea diferente según el género. En la era del aprendizaje profundo, nuestros modelos se ven afectados por los datos de entrenamiento que transfieren los prejuicios de los datos a los modelos. Los modelos de procesamiento del lenguaje natural pueden además amplificar este sesgo en los datos. Para comprender el efecto de la amplificación del prejuicio de género, se deben explorar enfoques de evaluación y mitigación. La sociedad científica ha visto la importancía de estas dos direcciones para posibilitar la propuesta de soluciones al problema. Nuestra tesis está dedicada a estas dos direcciones principales; proponiendo esquemas de evaluación, ya sea como conjuntos de datos y mecanismos de evaluación, además de sugerir técnicas de mitigación. Para la evaluación, propusimos técnicas para evaluar el prejuicio en representaciones vectoriales contextualizadas y modelos de traducción multilingüe. Además, presentamos puntos de referencia para evaluar el prejuicio de la traducción de voz y los modelos de traducción automática multilingüe. Para la dirección de mitigación, propusimos diferentes enfoques en los modelos de traducción automática agregando texto contextual, incrustaciones contextuales o relajando las restricciones de la arquitectura. Nuestros estudios de evaluación concluyeron que el prejuicio de género está fuertemente codificado en representaciones vectoriales contextuales que representan profesiones y sustantivos estereotipados. También revelamos que los algoritmos amplifican el sesgo y que la arquitectura del sistema afecta el comportamiento. Para efectos de evaluación, contribuimos a la creación de varios datos de referencia para fines de evaluación; presentamos un conjunto de datos que evalúa el sesgo de género en los sistemas de traducción de voz. Esta investigación sugiere que el estado actual de los sistemas de traducción del habla no nos permite evaluar con precisión el sesgo de género debido a la baja calidad de los sistemas de traducción del habla. Además, propusimos un conjunto de herramientas para construir conjuntos de datos equilibrados multilingües para entrenar y evaluar modelos NMT. Estos conjuntos de datos están equilibrados dentro de la ocupación de género. Descubrimos que los idiomas con muchos recursos generalmente tienden a predecir traducciones masculinas más precisas. Nuestros estudios de mitigación en NMT sugieren que se debe considerar la naturaleza de los conjuntos de datos y los idiomas para aplicar el enfoque correcto. La mitigación del sesgo puede basarse en agregar información contextual. Sin embargo, en otros casos, necesitamos repensar el modelo y relajar algunas condiciones que influyen en el sesgo que no afectan el rendimiento general pero reducen el efecto de la amplificación del sesgo.Postprint (published version

Evaluating Gender Bias in Speech Translation

The scientific community is increasingly aware of the necessity to embrace pluralism and consistently represent major and minor social groups. Currently, there are no standard evaluation techniques for different types of biases. Accordingly, there is an urgent need to provide evaluation sets and protocols to measure existing biases in our automatic systems. Evaluating the biases should be an essential step towards mitigating them in the systems. This paper introduces WinoST, a new freely available challenge set for evaluating gender bias in speech translation. WinoST is the speech version of WinoMT which is a MT challenge set and both follow an evaluation protocol to measure gender accuracy. Using a state-of-the-art end-to-end speech translation system, we report the gender bias evaluation on four language pairs and we show that gender accuracy in speech translation is more than 23% lower than in MT.Comment: Preprin

Evaluating the underlying gender bias in contextualized word embeddings

Gender bias is highly impacting natural language processing applications. Word embeddings have clearly been proven both to keep and amplify gender biases that are present in current data sources. Recently, contextualized word embeddings have enhanced previous word embedding techniques by computing word vector representations dependent on the sentence they appear in. In this paper, we study the impact of this conceptual change in the word embedding computation in relation with gender bias. Our analysis includes different measures previously applied in the literature to standard word embeddings. Our findings suggest that contextualized word embeddings are less biased than standard ones even when the latter are debiased.We want to thank Hila Gonen for her support dur-ing our research.This work is supported in part by the Catalan Agency for Management of University andResearch Grants (AGAUR) through the FI PhDScholarship and the Industrial PhD Grant. Thiswork is also supported in part by the Span-ish Ministerio de Economa y Competitividad, the European Regional Development Fund andthe Agencia Estatal de Investigacin, through thepostdoctoral senior grant Ramn y Cajal, contract TEC2015-69266-P (MINECO/FEDER,EU)and contract PCIN-2017-079 (AEI/MINECO).Peer ReviewedPostprint (published version

Gender Bias in Multilingual Neural Machine Translation: The Architecture Matters

Multilingual Neural Machine Translation architectures mainly differ in the amount of sharing modules and parameters among languages. In this paper, and from an algorithmic perspective, we explore if the chosen architecture, when trained with the same data, influences the gender bias accuracy. Experiments in four language pairs show that Language-Specific encoders-decoders exhibit less bias than the Shared encoder-decoder architecture. Further interpretability analysis of source embeddings and the attention shows that, in the Language-Specific case, the embeddings encode more gender information, and its attention is more diverted. Both behaviors help in mitigating gender bias.Comment: 12 pages, 5 figures, 3 table

The TALP-UPC participation in WMT21 news translation task: an mBART-based NMT approach

This paper describes the submission to the WMT 2021 news translation shared task by the UPC Machine Translation group. The goal of the task is to translate German to French (De-Fr) and French to German (Fr-De). Our submission focuses on fine-tuning a pre-trained model to take advantage of monolingual data. We fine-tune mBART50 using the filtered data, and additionally, we train a Transformer model on the same data from scratch. In the experiments, we show that fine-tuning mBART50 results in 31.69 BLEU for De-Fr and 23.63 BLEU for Fr-De, which increases 2.71 and 1.90 BLEU accordingly, as compared to the model we train from scratch. Our final submission is an ensemble of these two models, further increasing 0.3 BLEU for Fr-De.Postprint (published version

Design of a multi-center immunophenotyping analysis of peripheral blood, sputum and bronchoalveolar lavage fluid in the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Background Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS) is a multi-center longitudinal, observational study to identify novel phenotypes and biomarkers of chronic obstructive pulmonary disease (COPD). In a subset of 300 subjects enrolled at six clinical centers, we are performing flow cytometric analyses of leukocytes from induced sputum, bronchoalveolar lavage (BAL) and peripheral blood. To minimize several sources of variability, we use a “just-in-time” design that permits immediate staining without pre-fixation of samples, followed by centralized analysis on a single instrument. Methods The Immunophenotyping Core prepares 12-color antibody panels, which are shipped to the six Clinical Centers shortly before study visits. Sputum induction occurs at least two weeks before a bronchoscopy visit, at which time peripheral blood and bronchoalveolar lavage are collected. Immunostaining is performed at each clinical site on the day that the samples are collected. Samples are fixed and express shipped to the Immunophenotyping Core for data acquisition on a single modified LSR II flow cytometer. Results are analyzed using FACS Diva and FloJo software and cross-checked by Core scientists who are blinded to subject data. Results Thus far, a total of 152 sputum samples and 117 samples of blood and BAL have been returned to the Immunophenotyping Core. Initial quality checks indicate useable data from 126 sputum samples (83%), 106 blood samples (91%) and 91 BAL samples (78%). In all three sample types, we are able to identify and characterize the activation state or subset of multiple leukocyte cell populations (including CD4+ and CD8+ T cells, B cells, monocytes, macrophages, neutrophils and eosinophils), thereby demonstrating the validity of the antibody panel. Conclusions Our study design, which relies on bi-directional communication between clinical centers and the Core according to a pre-specified protocol, appears to reduce several sources of variability often seen in flow cytometric studies involving multiple clinical sites. Because leukocytes contribute to lung pathology in COPD, these analyses will help achieve SPIROMICS aims of identifying subgroups of patients with specific COPD phenotypes. Future analyses will correlate cell-surface markers on a given cell type with smoking history, spirometry, airway measurements, and other parameters. Trial registration This study was registered with ClinicalTrials.gov as NCT01969344

Comparison of Proteomic Assessment Methods in Multiple Cohort Studies

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155922/1/pmic13292_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155922/2/pmic13292.pd

Precision engineering for PRRSV resistance in pigs: Macrophages from genome edited pigs lacking CD163 SRCR5 domain are fully resistant to both PRRSV genotypes while maintaining biological function

Porcine Reproductive and Respiratory Syndrome (PRRS) is a panzootic infectious disease of pigs, causing major economic losses to the world-wide pig industry. PRRS manifests differently in pigs of all ages but primarily causes late-term abortions and stillbirths in sows and respiratory disease in piglets. The causative agent of the disease is the positive-strand RNA PRRS virus (PRRSV). PRRSV has a narrow host cell tropism, limited to cells of the monocyte/macrophage lineage. CD163 has been described as a fusion receptor for PRRSV, whereby the scavenger receptor cysteine-rich domain 5 (SRCR5) region was shown to be an interaction site for the virus in vitro. CD163 is expressed at high levels on the surface of macrophages, particularly in the respiratory system. Here we describe the application of CRISPR/Cas9 to pig zygotes, resulting in the generation of pigs with a deletion of Exon 7 of the CD163 gene, encoding SRCR5. Deletion of SRCR5 showed no adverse effects in pigs maintained under standard husbandry conditions with normal growth rates and complete blood counts observed. Pulmonary alveolar macrophages (PAMs) and peripheral blood monocytes (PBMCs) were isolated from the animals and assessed in vitro. Both PAMs and macrophages obtained from PBMCs by CSF1 stimulation (PMMs) show the characteristic differentiation and cell surface marker expression of macrophages of the respective origin. Expression and correct folding of the SRCR5 deletion CD163 on the surface of macrophages and biological activity of the protein as hemoglobin-haptoglobin scavenger was confirmed. Challenge of both PAMs and PMMs with PRRSV genotype 1, subtypes 1, 2, and 3 and PMMs with PRRSV genotype 2 showed complete resistance to viral infections assessed by replication. Confocal microscopy revealed the absence of replication structures in the SRCR5 CD163 deletion macrophages, indicating an inhibition of infection prior to gene expression, i.e. at entry/fusion or unpacking stages

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p 10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10−392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group