NICE : Neural Integrated Custom Engines (2020)

Abstract: In this paper, we present a machine translation system implemented by the Translation Centre for the Bodies of the European Union (CdT). The main goal of this project is to create domain-specific machine translation engines in order to support machine translation services and applications to the Translation Centre’s clients. In this article, we explain the entire implementation process of NICE: Neural Integrated Custom Engines. We describe the problems identified and the solutions provided, and present the final results for different language pairs. Finally, we describe the work that will be done on this project in the future.

## data preparation in this section, we describe the entire data preparation process, which is as follows: ## training all our engines are built with opennmt-tf @xcite , which is an open-source toolkit for nmt and neural sequence learning with a tensorflow backend. ## evaluation in this section, we describe the validation process and test set generation and which type of sentences each test set contains. we describe the final evaluation of the models by human translators and the benchmarking exercise against two state-of-the-art systems: etranslation and deepl pro. foot_2 ## deployment for deployment, weight pruning @xcite nice etranslation deepl 0 % 20 % 40 % 60 % 80 % 100 % acceptable as is little post-editing needed post-editing quicker than re-translation re-translation required figure 11: post-editing effort per segment, weighted by segment length. based on the calculation of the normalised edit distance at segment level weighted by the segment length in a total of 1 533 segments from documents of all agencies from the ph domain post-edited by cdt linguists. the ctranslate2 tool, which is an optimised inference engine for opennmt-py and opennmt-tf models supporting both cpu and gpu execution. 8 this library is geared towards an efficient serving of standard translation models, but is also a place for experimentation around model compression and inference acceleration. ## conclusions and future work this paper describes the implementation of nice: neural integrated custom engines, which was developed by cdt in collaboration with the european union intellectual property office (euipo). the system described in this article includes a sophisticated data preprocessing pipeline. different techniques for data filtering were applied with satisfactory results. depending on the language pair, we also applied data augmentation techniques, which improved the output quality. in this work, we focused on two domains: ip and ph. nevertheless, the system has been designed to allow the rapid implementation of new eu-related domains, such as legal or finance. of the 36 samples evaluated in both domains, 22 were fit for post-editing purposes. nice produced very satisfactory results for the ip domain and technical documents from the ph domain. nmt development is an iterative process and it can be assumed that quality will improve over time. the database of high-quality translations produced by cdt contractors and in-house translators is growing day by day. as the cdt collects more data from revised and post-edited translations, incremental learning will be applied @xcite . still, there is room for improvement by other means, such as named entity recognition (ner) @xcite . for example, ner can be applied in the ph domain, where the use of names of medicines and active substances is very frequent, and for which sentencepiece does not manage well, tending to create new words. another technique under development is the neural quality estimation for translation hypothesis selection @xcite , which allows the translation quality to be rated without references at run-time. finally, another technique is the application of advanced domain adaptation methods to enlarge ind datasets, mainly for languages with fewer resources. we are working to adapt a state-of-the-art classifier from @xcite for domain adaptation. the goal is to select more suitable pairs of sentences from the gen dataset and include them in ind. soon, other custom engines will be implemented for other domains, such as the legal domain. we are working on collecting data. the final step in this project will be to implement a simple web service that seamlessly integrates custom engines into the cdt's advanced translation workflows, allowing translators to work directly with our state-of-the-art, in-domain nmt technology nice.