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Found 10 papers, 5 papers with code
TalkUp: A Novel Dataset Paving the Way for Understanding Empowering Language
Empowering language is important in many real-world contexts, from education to workplace dynamics to healthcare.
Analyzing Norm Violations in Live-Stream Chat
Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter.
From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models
We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks.
Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media
NLP research on public opinion manipulation campaigns has primarily focused on detecting overt strategies such as fake news and disinformation.
Detecting Community Sensitive Norm Violations in Online Conversations
Online platforms and communities establish their own norms that govern what behavior is acceptable within the community.
Controlled Analyses of Social Biases in Wikipedia Bios
In this work, we present a methodology for analyzing Wikipedia pages about people that isolates dimensions of interest (e. g., gender), from other attributes (e. g., occupation).
Multilingual Contextual Affective Analysis of LGBT People Portrayals in Wikipedia
Specific lexical choices in narrative text reflect both the writer's attitudes towards people in the narrative and influence the audience's reactions.
NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer
This paper describes our approach to the task of identifying offensive languages in a multilingual setting.
Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated Tasks
Much work in cross-lingual transfer learning explored how to select better transfer languages for multilingual tasks, primarily focusing on typological and genealogical similarities between languages.
Learning to Generate Word- and Phrase-Embeddings for Efficient Phrase-Based Neural Machine Translation
In this paper, we introduce a phrase-based NMT model built upon continuous-output NMT, in which the decoder generates embeddings of words or phrases.
