Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userGenerating Gender Augmented Data for NLP
توليد البيانات المعززة بين الجنسين ل NLP
268
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
التحيز بين الجنسين هو حدوث متكرر في التطبيقات القائمة على البرامج الوطنية للتنمية الوطنية، وخاصة وضوحا باللغات التي يتم تنشيطها بين الجنسين. يمكن أن تظهر التحيز من خلال جمعيات بعض الصفات وتحريك الأسماء مع الجنس الطبيعي للإرازات، ولكن أيضا بسبب الترددات الجنسانية النحوية غير الموازنة من الكلمات المصدرة. يصبح هذا النوع من التحيز أكثر وضوحا في توليد ينصوصات التحدث حيث لا يتم تحديد النوع الاجتماعي داخل الجملة، لأن معظم تطبيقات NLP الحالية لا تزال تعمل على سياق مستوى الجملة. كخطوة نحو NLP أكثر شمولية، تقترح هذه الورقة نهجا تلقائيا وعموما إعادة كتابة جمل محادثة قصيرة. يمكن تطبيق طريقة إعادة كتابة على الجمل التي، دون سياق خارجي، لديها بدائل مكافئة متعددة من حيث الجنس. يمكن تطبيق الطريقة على إنشاء مخرجات متوازنة بين الجنسين وكذلك لإنشاء بيانات تدريب متوازنة بين الجنسين. يعتمد النهج المقترح على نظام ترجمة آلية عصبية مدربة على ترجمة "من نوع جنسي إلى آخر. إظهار كل من التحليل الأوتوماتيكي واليدوي للنهج النتائج الواعدة فيما يتعلق بالجيل التلقائي من البدائل الجنسانية لجمل المحادثة باللغة الإسبانية.
Gender bias is a frequent occurrence in NLP-based applications, especially pronounced in gender-inflected languages. Bias can appear through associations of certain adjectives and animate nouns with the natural gender of referents, but also due to unbalanced grammatical gender frequencies of inflected words. This type of bias becomes more evident in generating conversational utterances where gender is not specified within the sentence, because most current NLP applications still work on a sentence-level context. As a step towards more inclusive NLP, this paper proposes an automatic and generalisable re-writing approach for short conversational sentences. The rewriting method can be applied to sentences that, without extra-sentential context, have multiple equivalent alternatives in terms of gender. The method can be applied both for creating gender balanced outputs as well as for creating gender balanced training data. The proposed approach is based on a neural machine translation system trained to translate' from one gender alternative to another. Both the automatic and manual analysis of the approach show promising results with respect to the automatic generation of gender alternatives for conversational sentences in Spanish.
References used
https://aclanthology.org/
rate research
Read More
Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is much less understood whether, and how, predictions can be manipulated with small, concealed changes to the training data. In this work, we develop a new da
ta poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is present in the input. For instance, we insert 50 poison examples into a sentiment model's training set that causes the model to frequently predict Positive whenever the input contains James Bond''. Crucially, we craft these poison examples using a gradient-based procedure so that they do not mention the trigger phrase. We also apply our poison attack to language modeling (Apple iPhone'' triggers negative generations) and machine translation (iced coffee'' mistranslated as hot coffee''). We conclude by proposing three defenses that can mitigate our attack at some cost in prediction accuracy or extra human annotation.
A key part of the NLP ethics movement is responsible use of data, but exactly what that means or how it can be best achieved remain unclear. This position paper discusses the core legal and ethical principles for collection and sharing of textual dat
a, and the tensions between them. We propose a potential checklist for responsible data (re-)use that could both standardise the peer review of conference submissions, as well as enable a more in-depth view of published research across the community. Our proposal aims to contribute to the development of a consistent standard for data (re-)use, embraced across NLP conferences.
Neural networks are the state-of-the-art method of machine learning for many problems in NLP. Their success in machine translation and other NLP tasks is phenomenal, but their interpretability is challenging. We want to find out how neural networks r
epresent meaning. In order to do this, we propose to examine the distribution of meaning in the vector space representation of words in neural networks trained for NLP tasks. Furthermore, we propose to consider various theories of meaning in the philosophy of language and to find a methodology that would enable us to connect these areas.
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 b
een 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.
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
