3 citations · 5 across the 7 of their papers we have counts for
7 papers
Learning to Translate Ambiguous Terminology by Preference Optimization on Post-Edits
Nathaniel Berger, Johannes Eschbach-Dymanus, Miriam Exel +2
In real world translation scenarios, terminology is rarely one-to-one. Instead, multiple valid translations may appear in a terminology dictionary, but correctness of a translation…
Post-edits Are Preferences Too
Nathaniel Berger, Miriam Exel, Matthias Huck +1
Preference Optimization (PO) techniques are currently one of the state of the art techniques for fine-tuning large language models (LLMs) on pairwise preference feedback from human…
Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation
Nathaniel Berger, Stefan Riezler, Miriam Exel +1
While large language models (LLMs) pre-trained on massive amounts of unpaired language data have reached the state-of-the-art in machine translation (MT) of general domain texts, p…
Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training
Nathaniel Berger, Miriam Exel, Matthias Huck +1
Supervised learning in Neural Machine Translation (NMT) typically follows a teacher forcing paradigm where reference tokens constitute the conditioning context in the model's predi…
Don't Search for a Search Method -- Simple Heuristics Suffice for Adversarial Text Attacks
Nathaniel Berger, Stefan Riezler, Artem Sokolov +1
Recently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP). A central research topic has been the investigation of searc…
Sparse Perturbations for Improved Convergence in Stochastic Zeroth-Order Optimization
Mayumi Ohta, Nathaniel Berger, Artem Sokolov +1
Interest in stochastic zeroth-order (SZO) methods has recently been revived in black-box optimization scenarios such as adversarial black-box attacks to deep neural networks. SZO m…
Correct Me If You Can: Learning from Error Corrections and Markings
Julia Kreutzer, Nathaniel Berger, Stefan Riezler
Sequence-to-sequence learning involves a trade-off between signal strength and annotation cost of training data. For example, machine translation data range from costly expert-gene…