Lexical Micro-adaptation for Neural Machine Translation

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

We explored the following features to weight the relevance of the bitexts and the individual sentences: corpus weights, alignment scores, recency of the data with respect to the

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-

“Evaluating discourse phenomena in neural machine translation,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational

Ozan Caglayan, Mercedes García-Martínez, Adrien Bardet, Walid Aransa, Fethi Bougares, Loïc Barrault.. Laboratoire d’Informatique de l’Université du

Oracle scores indicate that gains in terms of BLEU score are still possible, even with a PBMT system built on in-domain data and with- out introducing new data during the