Fitting Machine Translation into Clients

PPML Different ML algorithms Different Privacy- preservation objectives Different architectures - Clustering [1] - Classification [2] - Association Rule Mining [3] ML output

Training topic models As in (Louis and Webber, 2014), we use Latent Dirichlet Allocation (LDA) (Blei et al., 2003) to train topic models on our monolingual biography data, taking

2 For each test set, models are assessed on their ability to rank the cor- rect translation of an ambiguous sentence higher than the incorrect translation, using the disam-

In this section, we present the results of the evaluation of the dictionaries generated using four different approaches: (i) by dumping the Apertium French- Spanish dictionary, (ii)

We run our experiments on English-Italian data, by comparing the performance of different neural APE models (batch and online) used to correct the output of NMT systems of

This paper received the best paper award at the 53rd Annual Meeting of the Associ- ation of Computational Linguistics and the 7th International Joint Conference on Natural

Kurohashi, “An empirical comparison of domain adaptation methods for neural machine translation,” in Proceedings of the 55th Annual Meeting of the Association for

Cette thèse s’intéresse à la Traduction Automatique Neuronale Factorisé (FNMT) qui repose sur l'idée d'utiliser la morphologie et la décomposition grammaticale des mots (lemmes