6 citations · 12 across the 9 of their papers we have counts for
11 papers
StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes
Awantee Deshpande, Dana Ruiter, Marius Mosbach +1
Analyzing ethnic or religious bias is important for improving fairness, accountability, and transparency of natural language processing models. However, many techniques rely on hum…
Exploiting Social Media Content for Self-Supervised Style Transfer
Dana Ruiter, Thomas Kleinbauer, Cristina España-Bonet +2
Recent research on style transfer takes inspiration from unsupervised neural machine translation (UNMT), learning from large amounts of non-parallel data by exploiting cycle consis…
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan +42
Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these dat…
Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online
Dana Ruiter, Liane Reiners, Ashwin Geet D'Sa +6
Even though hate speech (HS) online has been an important object of research in the last decade, most HS-related corpora over-simplify the phenomenon of hate by attempting to label…
EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT
Svetlana Tchistiakova, Jesujoba Alabi, Koel Dutta Chowdhury +2
We describe the EdinSaar submission to the shared task of Multilingual Low-Resource Translation for North Germanic Languages at the Sixth Conference on Machine Translation (WMT2021…
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages
Dana Ruiter, Dietrich Klakow, Josef van Genabith +1
For most language combinations, parallel data is either scarce or simply unavailable. To address this, unsupervised machine translation (UMT) exploits large amounts of monolingual…
Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces
Vanessa Hahn, Dana Ruiter, Thomas Kleinbauer +1
Hate speech and profanity detection suffer from data sparsity, especially for languages other than English, due to the subjective nature of the tasks and the resulting annotation i…
The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation
David I. Adelani, Dana Ruiter, Jesujoba O. Alabi +5
Massively multilingual machine translation (MT) has shown impressive capabilities, including zero and few-shot translation between low-resource language pairs. However, these model…
Emoji-Based Transfer Learning for Sentiment Tasks
Susann Boy, Dana Ruiter, Dietrich Klakow
Sentiment tasks such as hate speech detection and sentiment analysis, especially when performed on languages other than English, are often low-resource. In this study, we exploit t…
HUMAN: Hierarchical Universal Modular ANnotator
Moritz Wolf, Dana Ruiter, Ashwin Geet D'Sa +3
A lot of real-world phenomena are complex and cannot be captured by single task annotations. This causes a need for subsequent annotations, with interdependent questions and answer…
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation
Dana Ruiter, Josef van Genabith, Cristina España-Bonet
Self-supervised neural machine translation (SSNMT) jointly learns to identify and select suitable training data from comparable (rather than parallel) corpora and to translate, in…