Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA diachronic evaluation of gender asymmetry in euphemism
تقييم DIACHRONIC للاخلار بين الجنسين في طرد
233
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
استخدام الوعود هو سائق معروف لتغيير اللغة. وقد اقترح أن تستخدم النساء النساء أكثر من الرجال. على الرغم من وجود العديد من الدراسات التي تحقق في الاختلافات بين الجنسين في اللغة، إلا أن المطالبة المتعلقة باستخدام الاستحمام لم يتم اختبارها بشكل شامل عبر الزمن. إذا كانت النساء يستخدمن اللفائف أكثر، فقد يعني ذلك أن النساء يقودن أيضا تكوين عمليات إفراطية جديدة وتغيير اللغة مع مرور الوقت. باستخدام أربعة نص شهير كوربورا كبيرة من اللغة الإنجليزية، نقيم الادعاء بأن النساء يستخدمن الزيادات أكثر من الرجال من خلال تحليل كمي. كنا قمنا بتجميع قائمة ب 106 أزواج من الطبع - المحرمات لتحليل استخدامها النسبي عبر الزمن من قبل كل جنس في Corga. على عكس الاعتقاد الحالي، تظهر نتائجنا أن النساء لا يستخدمن اللفائف ذات نسبة أعلى من الرجال. كررنا التحليل باستخدام قائمة فرعية مختلفة من قائمة الأزواج من الادعاء-المحرمات ووجدت أن النتيجة كانت قوية. تشير دراستنا إلى أنه في مجموعة واسعة من الإعدادات التي تنطوي على كل من الخطاب والكتابة، ومع درجات متفاوتة من الأشكال، لا تستخدم النساء أو تشكيل الزيادات أكثر من الرجال.
The use of euphemisms is a known driver of language change. It has been proposed that women use euphemisms more than men. Although there have been several studies investigating gender differences in language, the claim about euphemism usage has not been tested comprehensively through time. If women do use euphemisms more, this could mean that women also lead the formation of new euphemisms and language change over time. Using four large diachronic text corpora of English, we evaluate the claim that women use euphemisms more than men through a quantitative analysis. We assembled a list of 106 euphemism-taboo pairs to analyze their relative use through time by each gender in the corpora. Contrary to the existing belief, our results show that women do not use euphemisms with a higher proportion than men. We repeated the analysis using different subsets of the euphemism-taboo pairs list and found that our result was robust. Our study indicates that in a broad range of settings involving both speech and writing, and with varying degrees of formality, women do not use or form euphemisms more than men.
References used
https://aclanthology.org/
rate research
Read More
The vast majority of the existing approaches for taxonomy enrichment apply word embeddings as they have proven to accumulate contexts (in a broad sense) extracted from texts which are sufficient for attaching orphan words to the taxonomy. On the othe
r hand, apart from being large lexical and semantic resources, taxonomies are graph structures. Combining word embeddings with graph structure of taxonomy could be of use for predicting taxonomic relations. In this paper we compare several approaches for attaching new words to the existing taxonomy which are based on the graph representations with the one that relies on fastText embeddings. We test all methods on Russian and English datasets, but they could be also applied to other wordnets and languages.
With language models being deployed increasingly in the real world, it is essential to address the issue of the fairness of their outputs. The word embedding representations of these language models often implicitly draw unwanted associations that fo
rm a social bias within the model. The nature of gendered languages like Hindi, poses an additional problem to the quantification and mitigation of bias, owing to the change in the form of the words in the sentence, based on the gender of the subject. Additionally, there is sparse work done in the realm of measuring and debiasing systems for Indic languages. In our work, we attempt to evaluate and quantify the gender bias within a Hindi-English machine translation system. We implement a modified version of the existing TGBI metric based on the grammatical considerations for Hindi. We also compare and contrast the resulting bias measurements across multiple metrics for pre-trained embeddings and the ones learned by our machine translation model.
Potential gender biases existing in Wikipedia's content can contribute to biased behaviors in a variety of downstream NLP systems. Yet, efforts in understanding what inequalities in portraying women and men occur in Wikipedia focused so far only on *
biographies*, leaving open the question of how often such harmful patterns occur in other topics. In this paper, we investigate gender-related asymmetries in Wikipedia titles from *all domains*. We assess that for only half of gender-related articles, i.e., articles with words such as *women* or *male* in their titles, symmetrical counterparts describing the same concept for the other gender (and clearly stating it in their titles) exist. Among the remaining imbalanced cases, the vast majority of articles concern sports- and social-related issues. We provide insights on how such asymmetries can influence other Wikipedia components and propose steps towards reducing the frequency of observed patterns.
Fake news causes significant damage to society. To deal with these fake news, several studies on building detection models and arranging datasets have been conducted. Most of the fake news datasets depend on a specific time period. Consequently, the
detection models trained on such a dataset have difficulty detecting novel fake news generated by political changes and social changes; they may possibly result in biased output from the input, including specific person names and organizational names. We refer to this problem as Diachronic Bias because it is caused by the creation date of news in each dataset. In this study, we confirm the bias, especially proper nouns including person names, from the deviation of phrase appearances in each dataset. Based on these findings, we propose masking methods using Wikidata to mitigate the influence of person names and validate whether they make fake news detection models robust through experiments with in-domain and out-of-domain data.
Internet search affects people's cognition of the world, so mitigating biases in search results and learning fair models is imperative for social good. We study a unique gender bias in image search in this work: the search images are often gender-imb
alanced for gender-neutral natural language queries. We diagnose two typical image search models, the specialized model trained on in-domain datasets and the generalized representation model pre-trained on massive image and text data across the internet. Both models suffer from severe gender bias. Therefore, we introduce two novel debiasing approaches: an in-processing fair sampling method to address the gender imbalance issue for training models, and a post-processing feature clipping method base on mutual information to debias multimodal representations of pre-trained models. Extensive experiments on MS-COCO and Flickr30K benchmarks show that our methods significantly reduce the gender bias in image search models.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
