2 citations · 3 across the 9 of their papers we have counts for
9 papers
OmniFusion: Simultaneous Multilingual Multimodal Translations via Modular Fusion
Sai Koneru, Matthias Huck, Jan Niehues
There has been significant progress in open-source text-only translation large language models (LLMs) with better language coverage and quality. However, these models can be only u…
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…
Quality-Aware Decoding: Unifying Quality Estimation and Decoding
Sai Koneru, Matthias Huck, Miriam Exel +1
Quality Estimation (QE) models for Neural Machine Translation (NMT) predict the quality of the hypothesis without having access to the reference. An emerging research direction in…
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…
Plug, Play, and Fuse: Zero-Shot Joint Decoding via Word-Level Re-ranking Across Diverse Vocabularies
Sai Koneru, Matthias Huck, Miriam Exel +1
Recent advancements in NLP have resulted in models with specialized strengths, such as processing multimodal inputs or excelling in specific domains. However, real-world tasks, lik…
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…
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing
Sai Koneru, Miriam Exel, Matthias Huck +1
Large Language Models (LLM's) have demonstrated considerable success in various Natural Language Processing tasks, but they have yet to attain state-of-the-art performance in Neura…
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…
Modeling Target-Side Morphology in Neural Machine Translation: A Comparison of Strategies
Marion Weller-Di Marco, Matthias Huck, Alexander Fraser
Morphologically rich languages pose difficulties to machine translation. Machine translation engines that rely on statistical learning from parallel training data, such as state-of…