Specifically mBERT, DistilmBERT and XLM-R distilled with knowledge from SBERT-nli-stsb, which will be referred to as. Multilingual models trained through the proposed knowledge distillation process. some European languages included in Europarl1 and OPUS.2 Furthermore, the existing. Multilingual Universal Sentence Encoder (mUSE), a dual-encoder transformer architecture trained on SNLI data and parallel corpora over 16 languages. Unfortunately, parallel texts are a sparse resource for many. A parallel corpus, also called bitext, consists in bilingual/multilingual texts aligned at the sentence level. Finally, we create the ML50 benchmark, covering low, mid, and high resource languages, to facilitate reproducible research by standardizing training and evaluation data. the translation model and monolingual data to build the target language model. 3.2 Creating the multilingual k-nn graph In the framework of the WMT evaluations,5 large collec-tions of monolingual news texts are provided. For each mini-batch, one language pair is selected. monolingual and parallel data processing opera-tions. OpusFilter, focusing on processing data col-lected in OPUS (Tiedemann,2012), the world’s largest resource of openly available parallel cor-pora. In this case, Bitext has one of the widest lexical and morphological databases covering 50+ languages (and increasing every day). Bitext seg- ments can be ranked according to their qual. We double the number of languages in mBART to support multilingual machine translation models of 50 languages. available on the OPUS web site (Tiedemann 2012).4 This implements multi-task training with two target languages. It is relatively easy to find and produce monolingual training sets for language identification but the task to produce the same training sets for multilingual content has proven to be not that easy. We demonstrate that pretrained models can be extended to incorporate additional languages without loss of performance. 1 Introduction Cross-lingual sentence retrieval aims at aligning parallel sentence pairs that are translations of each other from unlabeled multilingual documents. are connected with massively multilingual data sets and data-driven natural. Compared to multilingual models trained from scratch, starting from pretrained models incorporates the benefits of large quantities of unlabeled monolingual data, which is particularly important for low resource languages where bitext is not available. monolingual and parallel data size, up to a cer-tain size threshold, rather than on what lan-guage pairs are used for training or evaluation. In this talk I will discuss the current state of OPUS-MT, our project on. Instead of finetuning on one direction, a pretrained model is finetuned on many directions at the same time. In this work, we show that multilingual translation models can be created through multilingual finetuning. Previous work in multilingual pretraining has demonstrated that machine translation systems can be created by finetuning on bitext.

Recent work demonstrates the potential of multilingual pretraining of creating one model that can be used for various tasks in different languages.

Opus bitext and monolingual data