Dictionary-based Domain Adaptation of MT Systems without Retraining

We describe our submission to the IT-domain translation task of WMT 2016. We perform domain adaptation with dictionary data on already trained MT systems with no further retraining. We apply our approach to two conceptually different systems developed within the QTLeap project: TectoMT and Moses, as well as Chimera, their combination. In all settings, our method improves the translation quality. Moreover, the basic variant of our approach is applicable to any MT system, including a black-box one

Domain adaptation : Retraining NMT with translation memories

The topic of this thesis is domain adaptation of an NMT system by retraining it with translation memories. The translation memory used in the experiments is the EMEA corpus that consists of medical texts – mostly package leaflets. The NMT system used in the experiments is OpenNMT because it is completely free and easy to use. The goal of this thesis is to find out how an NMT system can be adapted to a special domain, and if the translation quality improves after domain adaptation. The original plan was to continue training the pretrained model of OpenNMT with EMEA data, but this is not possible. Therefore, it is necessary to train a new baseline model with the same data as the pretrained model was trained with. After this two domain adaptation methods are tested: continuation training with EMEA data and continuation training with unknown terms. In the manual evaluation, it turned out that domain adaptation with unknown terms worsens the translation quality drastically because all sentences are translated as single words. This method is only suitable for translating wordlists because it improved the translation of unknown terms. Domain adaptation with EMEA data, for the other hand, improves the translation quality significantly. The EMEA-retrained system translates long sentences and medical terms much better than the pretrained and the baseline models. Long and complicated terms are still difficult to translate but the EMEA-retrained model makes fewer errors than the other models. The evaluation metrics used for automatic evaluation are BLEU and LeBLEU. BLEU is stricter than LeBLEU. The results are similar as in the manual evaluation: The EMEA-retrained model translates medical texts much better than the other models, and the translation quality of the UNK-retrained model is the worst of all. It can be presumed that an NMT system needs contextual information so that it learns to translate terms and long sentences without transforming the text into a wordlist without sentences. In addition, it seems that long terms are translated in smaller pieces so that the NMT system possibly translates some pieces wrong, which results in that the whole term is wrong

MR image reconstruction using deep density priors

Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data. Deep learning (DL) provides a powerful framework for extracting such information from existing image datasets, through learning, and then using it for reconstruction. Leveraging this, recent methods employed DL to learn mappings from undersampled to fully sampled images using paired datasets, including undersampled and corresponding fully sampled images, integrating prior knowledge implicitly. In this article, we propose an alternative approach that learns the probability distribution of fully sampled MR images using unsupervised DL, specifically Variational Autoencoders (VAE), and use this as an explicit prior term in reconstruction, completely decoupling the encoding operation from the prior. The resulting reconstruction algorithm enjoys a powerful image prior to compensate for missing k-space data without requiring paired datasets for training nor being prone to associated sensitivities, such as deviations in undersampling patterns used in training and test time or coil settings. We evaluated the proposed method with T1 weighted images from a publicly available dataset, multi-coil complex images acquired from healthy volunteers (N=8) and images with white matter lesions. The proposed algorithm, using the VAE prior, produced visually high quality reconstructions and achieved low RMSE values, outperforming most of the alternative methods on the same dataset. On multi-coil complex data, the algorithm yielded accurate magnitude and phase reconstruction results. In the experiments on images with white matter lesions, the method faithfully reconstructed the lesions. Keywords: Reconstruction, MRI, prior probability, machine learning, deep learning, unsupervised learning, density estimationComment: Published in IEEE TMI. Main text and supplementary material, 19 pages tota