Search Results for author: Miriam Exel

Found 10 papers, 1 papers with code

Terminology-Constrained Neural Machine Translation at SAP

no code implementations EAMT 2020 Miriam Exel, Bianka Buschbeck, Lauritz Brandt, Simona Doneva

This paper examines approaches to bias a neural machine translation model to adhere to terminology constraints in an industrial setup.

Machine Translation Translation

Quality-Aware Decoding: Unifying Quality Estimation and Decoding

no code implementations12 Feb 2025 Sai Koneru, Matthias Huck, Miriam Exel, Jan Niehues

An emerging research direction in NMT involves the use of Quality Estimation (QE) models, which have demonstrated high correlations with human judgment and can enhance translations through Quality-Aware Decoding.

Decoder Document Translation +3

Post-edits Are Preferences Too

no code implementations3 Oct 2024 Nathaniel Berger, Stefan Riezler, Miriam Exel, Matthias Huck

We attempt to use these implicit preferences for PO and show that it helps the model move towards post-edit-like hypotheses and away from machine translation-like hypotheses.

Machine Translation Translation

Plug, Play, and Fuse: Zero-Shot Joint Decoding via Word-Level Re-ranking Across Diverse Vocabularies

no code implementations21 Aug 2024 Sai Koneru, Matthias Huck, Miriam Exel, Jan Niehues

However, real-world tasks, like multimodal translation, often require a combination of these strengths, such as handling both translation and image processing.

Machine Translation Re-Ranking +1

Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation

no code implementations4 Jun 2024 Nathaniel Berger, Stefan Riezler, Miriam Exel, Matthias Huck

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, post-editing (PE) is still required to correct errors and to enhance term translation quality in specialized domains.

Machine Translation Translation

Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing

no code implementations23 Oct 2023 Sai Koneru, Miriam Exel, Matthias Huck, Jan Niehues

Building on the LLM's exceptional ability to process and generate lengthy sequences, we also propose extending our approach to document-level translation.

Machine Translation NMT +3

Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training

no code implementations17 Jul 2023 Nathaniel Berger, Miriam Exel, Matthias Huck, Stefan Riezler

Supervised learning in Neural Machine Translation (NMT) typically follows a teacher forcing paradigm where reference tokens constitute the conditioning context in the model's prediction, instead of its own previous predictions.

Machine Translation NMT +1

A Parallel Evaluation Data Set of Software Documentation with Document Structure Annotation

1 code implementation AACL (WAT) 2020 Bianka Buschbeck, Miriam Exel

This paper accompanies the software documentation data set for machine translation, a parallel evaluation data set of data originating from the SAP Help Portal, that we released to the machine translation community for research purposes.

Machine Translation Translation

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