Papers by Thanh-Le Ha
Overcoming the Rare Word Problem for low-resource language pairs in Neural Machine Translation (D19-52)
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| Challenge: | Despite above approaches can improve the prediction of rare words, they still have challenges which have adverse effects on its effectiveness. |
| Approach: | They propose three ways to address rare-word problem in neural machine translation systems . they propose an algorithm to learn morphology of unknown words for English in supervised way to minimize adverse effect of rare- word problem. |
| Outcome: | The proposed approaches improve accuracy on two low-resource language pairs. |
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. |
KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus (L18-1)
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| Challenge: | Cross-lingual word embeddings are representations of words across languages in a shared continuous vector space. |
| Approach: | They propose a multilingual word embedding corpus which is acquired by neural machine translation and is based on monolingual data. |
| Outcome: | The proposed method is competitive with existing methods but on the cross-lingual document classification task, it obtains the best figures. |
Gradient-based Gradual Pruning for Language-Specific Multilingual Neural Machine Translation (2023.emnlp-main)
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| Challenge: | Multilingual neural machine translation suffers from performance degradation in high-resource languages compared to bilingual counterparts. |
| Approach: | They propose a gradient-based gradual pruning technique for multilingual neural machine translation that allows for partial parameter sharing across language pairs to alleviate interference. |
| Outcome: | The proposed approach yields a notable performance gain on IWSLT and WMT datasets. |