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Found 17 papers, 9 papers with code
The Effect of Domain and Diacritics in Yoruba–English Neural Machine Translation
Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs.
UdS-DFKI@WMT20: Unsupervised MT and Very Low Resource Supervised MT for German-Upper Sorbian
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).
Label Propagation-Based Semi-Supervised Learning for Hate Speech Classification
In this paper, label propagation-based semi-supervised learning is explored for the task of hate speech classification.
StereoKG: Data-Driven Knowledge Graph Construction for Cultural Knowledge and Stereotypes
Analyzing ethnic or religious bias is important for improving fairness, accountability, and transparency of natural language processing models.
Exploiting Social Media Content for Self-Supervised Style Transfer
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.
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
We focus on two questions: 1) How can pre-trained models be used for languages not included in the initial pre-training?
Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online
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".
EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT
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).
Modeling Profanity and Hate Speech in Social Media with Semantic Subspaces
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.
The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation
We investigate how and when this training condition affects the final quality and intelligibility of a translation.
Emoji-Based Transfer Learning for Sentiment Tasks
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.
HUMAN: Hierarchical Universal Modular ANnotator
A lot of real-world phenomena are complex and cannot be captured by single task annotations.
Self-Induced Curriculum Learning in Self-Supervised Neural Machine Translation
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.
Self-Induced Curriculum Learning in Neural Machine Translation
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.
UdS-DFKI Participation at WMT 2019: Low-Resource (en-gu) and Coreference-Aware (en-de) Systems
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).
Self-Supervised Neural Machine Translation
We present a simple new method where an emergent NMT system is used for simultaneously selecting training data and learning internal NMT representations.
