Papers by Matthias Huck
Learning to Translate Ambiguous Terminology by Preference Optimization on Post-Edits (2025.emnlp-industry)
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| Challenge: | Ambiguous terminology can make translation difficult, especially in corporate contexts. |
| Approach: | They propose to learn how to disambiguate terminology based on human post-edits . they use preference optimization to optimize for correctness using the term post-Edit . |
| Outcome: | The proposed framework improves term accuracy over a translation oriented LLM without significant losses in COMET score. |
Better OOV Translation with Bilingual Terminology Mining (P19-1)
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| Challenge: | Unseen words, also called out-of-vocabulary words, are difficult for machine translation . byte-pair encoding can be used to represent OOVs, but they are often incorrectly translated . |
| Approach: | They propose to use monolingual data to improve the translation of unseen words . they use five target language words to mine target-language sentences . |
| Outcome: | The proposed system can be used to improve translation of out-of-vocabulary words (OOVs) the proposed system is trained on Europarl and can be fine-tuned to improve the translation quality. |
A Multilingual Multiway Evaluation Data Set for Structured Document Translation of Asian Languages (2022.findings-aacl)
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| Challenge: | a lack of evaluation data sets for structured content limits progress in machine translation . a common use case of machine translation is the translation of structured or formatted documents . |
| Approach: | They propose a multilingual multiway evaluation data set for machine translation of structured documents of Asian languages Japanese, Korean and Chinese. |
| Outcome: | The proposed data set is well suited for multilingual evaluation and contains richer annotation tag sets than existing data sets. |
Analyzing Challenges in Neural Machine Translation for Software Localization (2023.eacl-main)
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| Challenge: | Neural machine translation (NMT) is a new form of machine translation that reduces the post-editing time of human annotators. |
| Approach: | They propose to use a novel multilingual UI corpus collection to test NMT for user interfaces. |
| Outcome: | The proposed test set evaluates state-of-the-art methods on a UI translation task from English to German and identifies its limitations. |
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing (2024.naacl-long)
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| Challenge: | Large language models have demonstrated considerable success in various natural language processing tasks, but their performance in NMT tasks is still underexplored. |
| Approach: | They propose to use LLMs as automatic post-editors rather than direct translators to improve BLEU and COMET performance. |
| Outcome: | The proposed approach improves BLEU but COMET performance compared to in-context learning. |