Search Results for author: Dana Ruiter

Found 16 papers, 7 papers with code

UdS-DFKI@WMT20: Unsupervised MT and Very Low Resource Supervised MT for German-Upper Sorbian

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).

Translation Unsupervised Machine Translation

Exploiting Social Media Content for Self-Supervised Style Transfer

1 code implementation18 May 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.

Denoising Machine Translation +2

Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online

1 code implementation28 Apr 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".

Hate Speech Detection

EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT

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).

Machine Translation Translation

Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces

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.

Emoji-Based Transfer Learning for Sentiment Tasks

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.

Hate Speech Detection Sentiment Analysis +1

Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation

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.

Denoising Machine Translation +1

Self-Induced Curriculum Learning in Neural Machine Translation

no code implementations25 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.

Denoising Machine Translation +1

UdS-DFKI Participation at WMT 2019: Low-Resource (en-gu) and Coreference-Aware (en-de) Systems

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).

Translation

Self-Supervised Neural Machine Translation

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.

Machine Translation Translation

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