Papers by Thomas Zenkel
End-to-End Neural Word Alignment Outperforms GIZA++ (2020.acl-main)
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| Challenge: | Word alignment was once a core unsupervised learning task in natural language processing . but word alignment still plays an important role in interactive applications of neural machine translation, such as annotation transfer and lexicon injection. |
| Approach: | They propose to use a Transformer model to train an unsupervised word alignment model. |
| Outcome: | The proposed method outperforms GIZA++ on three data sets and is tightly integrated and does not affect translation quality. |
Automatic Bilingual Markup Transfer (2021.findings-emnlp)
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| Challenge: | Existing work on markup transfer is performed with machine translation . a human translator generates the target translation without markup, and then the system infers the placement of markup tags. |
| Approach: | They propose two metrics and evaluate several approaches to bilingual markup transfer . best approach achieves an average accuracy of 94.7% across six language pairs . |
| Outcome: | The proposed approach achieves an average accuracy of 94.7% across six language pairs . it is a novel approach that can be applied to a structured document translation corpus . |
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning (C18-2)
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Florian Dessloch, Thanh-Le Ha, Markus Müller, Jan Niehues, Thai-Son Nguyen, Ngoc-Quan Pham, Elizabeth Salesky, Matthias Sperber, Sebastian Stüker, Thomas Zenkel, Alexander Waibel
| Challenge: | In today's globalized world, communication is difficult and often the language barrier still prevents communication. |
| Approach: | They have developed a low-latency translation system that is adapted to lectures and covers several language pairs. |
| Outcome: | The proposed system improves performance but also covers several European languages. |