English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Rule-based machine translation is a machine translation paradigm wherelinguistic knowledge is encoded by an expert in the form of rules thattranslate text from source to target language. While this approach grantsextensive control over the output of the system, the cost of formalising theneeded linguistic knowledge is much higher than training a corpus-based system,where a machine learning approach is used to automatically learn to translatefrom examples. In this paper, we describe different approaches to leverage theinformation contained in rule-based machine translation systems to improve acorpus-based one, namely, a neural machine translation model, with a focus on alow-resource scenario. Three different kinds of information were used:morphological information, named entities and terminology. In addition toevaluating the general performance of the system, we systematically analysedthe performance of the proposed approaches when dealing with the targetedphenomena. Our results suggest that the proposed models have limited ability tolearn from external information, and most approaches do not significantly alterthe results of the automatic evaluation, but our preliminary qualitativeevaluation shows that in certain cases the hypothesis generated by our systemexhibit favourable behaviour such as keeping the use of passive voice.

Aspects of Terminological and Named Entity Knowledge within Rule-Based  Machine Translation Models for Under-Resourced Neural Machine Translation  Scenarios