1 code implementation • NAACL 2022 • David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan, Julia Kreutzer, Xiaoyu Shen, Machel Reid, Dana Ruiter, Dietrich Klakow, Peter Nabende, Ernie Chang, Tajuddeen Gwadabe, Freshia Sackey, Bonaventure F. P. Dossou, Chris Chinenye Emezue, Colin Leong, Michael Beukman, Shamsuddeen Hassan Muhammad, Guyo Dub Jarso, Oreen Yousuf, Andre Niyongabo Rubungo, Gilles Hacheme, Eric Peter Wairagala, Muhammad Umair Nasir, Benjamin Ayoade Ajibade, Tunde Oluwaseyi Ajayi, Yvonne Wambui Gitau, Jade Abbott, Mohamed Ahmed, Millicent Ochieng, Anuoluwapo Aremu, Perez Ogayo, Jonathan Mukiibi, Fatoumata Ouoba Kabore, Godson Koffi Kalipe, Derguene Mbaye, Allahsera Auguste Tapo, Victoire Memdjokam Koagne, Edwin Munkoh-Buabeng, Valencia Wagner, Idris Abdulmumin, Ayodele Awokoya, Happy Buzaaba, Blessing Sibanda, Andiswa Bukula, Sam Manthalu
We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pre-training?
1 code implementation • EMNLP 2020 • Moritz Wolf, Dana Ruiter, Ashwin Geet D'Sa, Liane Reiners, Jan Alexandersson, Dietrich Klakow
A lot of real-world phenomena are complex and cannot be captured by single task annotations.
1 code implementation • NAACL (WOAH) 2022 • Awantee Deshpande, Dana Ruiter, Marius Mosbach, Dietrich Klakow
Analyzing ethnic or religious bias is important for improving fairness, accountability, and transparency of natural language processing models.
1 code implementation • ACL 2019 • Dana Ruiter, Cristina Espa{\~n}a-Bonet, Josef van Genabith
We present a simple new method where an emergent NMT system is used for simultaneously selecting training data and learning internal NMT representations.
1 code implementation • 15 Mar 2021 • David I. Adelani, Dana Ruiter, Jesujoba O. Alabi, Damilola Adebonojo, Adesina Ayeni, Mofe Adeyemi, Ayodele Awokoya, Cristina España-Bonet
We investigate how and when this training condition affects the final quality and intelligibility of a translation.
1 code implementation • EACL 2021 • Susann Boy, Dana Ruiter, Dietrich Klakow
This is done using a transfer learning approach, where the parameters learned by an emoji-based source task are transferred to a sentiment target task.
1 code implementation • ACL (WOAH) 2021 • Vanessa Hahn, Dana Ruiter, Thomas Kleinbauer, Dietrich Klakow
We observe that, on both similar and distant target tasks and across all languages, the subspace-based representations transfer more effectively than standard BERT representations in the zero-shot setting, with improvements between F1 +10. 9 and F1 +42. 9 over the baselines across all tested monolingual and cross-lingual scenarios.
1 code implementation • LREC 2022 • Dana Ruiter, Liane Reiners, Ashwin Geet D'Sa, Thomas Kleinbauer, Dominique Fohr, Irina Illina, Dietrich Klakow, Christian Schemer, Angeliki Monnier
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 user comments as "hate" or "neutral".
no code implementations • WS 2019 • Cristina Espa{\~n}a-Bonet, Dana Ruiter
This paper describes the UdS-DFKI submission to the WMT2019 news translation task for Gujarati{--}English (low-resourced pair) and German{--}English (document-level evaluation).
no code implementations • EMNLP 2020 • 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 a way that the two tasks support each other in a virtuous circle.
no code implementations • MTSummit 2021 • Dana Ruiter, Dietrich Klakow, Josef van Genabith, Cristina España-Bonet
For most language combinations, parallel data is either scarce or simply unavailable.
no code implementations • WMT (EMNLP) 2021 • Svetlana Tchistiakova, Jesujoba Alabi, Koel Dutta Chowdhury, Sourav Dutta, Dana Ruiter
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).
no code implementations • EMNLP (insights) 2020 • Ashwin Geet D’Sa, Irina Illina, Dominique Fohr, Dietrich Klakow, Dana Ruiter
In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification.
no code implementations • MTSummit 2021 • David Adelani, Dana Ruiter, Jesujoba Alabi, Damilola Adebonojo, Adesina Ayeni, Mofe Adeyemi, Ayodele Esther Awokoya, Cristina España-Bonet
Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs.
no code implementations • WMT (EMNLP) 2020 • Sourav Dutta, Jesujoba Alabi, Saptarashmi Bandyopadhyay, Dana Ruiter, Josef van Genabith
This paper describes the UdS-DFKI submission to the shared task for unsupervised machine translation (MT) and very low-resource supervised MT between German (de) and Upper Sorbian (hsb) at the Fifth Conference of Machine Translation (WMT20).
no code implementations • 25 Sep 2019 • Dana Ruiter, Cristina España-Bonet, Josef van Genabith
Self-supervised neural machine translation (SS-NMT) learns how to extract/select suitable training data from comparable (rather than parallel) corpora and how to translate, in a way that the two tasks support each other in a virtuous circle.
1 code implementation • NAACL (SocialNLP) 2022 • Dana Ruiter, Thomas Kleinbauer, Cristina España-Bonet, Josef van Genabith, Dietrich Klakow
Recent research on style transfer takes inspiration from unsupervised neural machine translation (UNMT), learning from large amounts of non-parallel data by exploiting cycle consistency loss, back-translation, and denoising autoencoders.