Papers by MinhQuang Pham
SYSTRAN @ WAT 2019: Russian-Japanese News Commentary task (D19-52)
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| Challenge: | Several data sets were provided for the task, but the data was limited and the distance and richness of the language pair were very challenging. |
| Approach: | They describe the SYSTRAN neural MT systems employed for the 6th Workshop on Asian Translation (WAT) they use the neural Transformer architecture learned over the provided resources and perform synthetic data generation experiments which aim at alleviating the data scarcity problem. |
| Outcome: | The proposed systems rank first according to automatic evaluations based on the neural Transformer architecture learned over the provided resources. |
Fixing Translation Divergences in Parallel Corpora for Neural MT (D18-1)
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| Challenge: | Existing methods to detect translation divergences from parallel corpora are noisy and limited in size. |
| Approach: | They propose an unsupervised method for detecting translation divergences in parallel sentences . they use a neural network that computes cross-lingual sentence similarity scores . |
| Outcome: | The proposed method improves translation performance for English-French and English-German translation tasks. |
Revisiting Multi-Domain Machine Translation (2021.tacl-1)
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| Challenge: | Existing approaches to handle multi-domain machine translation systems are lacking due to the variability of data. |
| Approach: | They propose to use domain adaptation methods to handle situations where a sample of matched sentences is available in training and where only samples of source-side sentences are available. |
| Outcome: | The proposed model is able to handle multiple domains and their expectations with respect to performance. |