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Found 10 papers, 1 papers with code
Incorporating External Annotation to improve Named Entity Translation in NMT
The correct translation of named entities (NEs) still poses a challenge for conventional neural machine translation (NMT) systems.
Terminology-Constrained Neural Machine Translation at SAP
This paper examines approaches to bias a neural machine translation model to adhere to terminology constraints in an industrial setup.
“Hi, how can I help you?” Improving Machine Translation of Conversational Content in a Business Context
This paper addresses the automatic translation of conversational content in a business context, for example support chat dialogues.
Quality-Aware Decoding: Unifying Quality Estimation and Decoding
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.
Post-edits Are Preferences Too
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.
Plug, Play, and Fuse: Zero-Shot Joint Decoding via Word-Level Re-ranking Across Diverse Vocabularies
However, real-world tasks, like multimodal translation, often require a combination of these strengths, such as handling both translation and image processing.
Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation
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.
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing
Building on the LLM's exceptional ability to process and generate lengthy sequences, we also propose extending our approach to document-level translation.
Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training
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.
A Parallel Evaluation Data Set of Software Documentation with Document Structure Annotation
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.
