This paper provides an overview of the main achievements of the completed PRINCIPLE project, a 2-year action funded by the European Commission under the Connecting Europe Facility (CEF) programme. PRINCIPLE focused on collecting high-quality language resources for Croatian, Icelandic, Irish and Norwegian, which are severely low-resource languages, especially for building effective machine translation (MT) systems. We report the achievements of the project, primarily, in terms of the large amounts of data collected for all four low-resource languages and of promoting the uptake of neural MT (NMT) for these languages.

This paper updates the progress made on the PRINCIPLE project, a 2-year action funded by the European Commission under the Connecting Europe Facility (CEF) programme. PRINCIPLE focuses on collecting high-quality language resources for Croatian, Icelandic, Irish and Norwegian, which have been identified as low-resource languages, especially for building effective machine translation (MT) systems. We report initial achievements of the project and ongoing activities aimed at promoting the uptake of neural MT for the low-resource languages of the project.

Intellectual Property professionals frequently need to carry out patent searches for a variety of reasons. During a typical search, they will retrieve approximately 30% of their results in a foreign language. The machine translation (MT) options currently available to patent searchers for these foreign-language patents vary in their quality, consistency, and general level of service. In this article, we introduce IPTranslator; an MT web service designed to cater for the needs of patent searchers. At the core of IPTranslator is a set of MT systems developed specifically for translating patent text. We describe the challenges faced in adapting MT technology to such a complex domain, and how the systems were evaluated to ensure that the quality was fit for purpose. Finally, we present the framework through which the IPTranslator service is delivered to users, and the value-adding features which address many of the issues with existing solutions.

PLuTO – Patent Language Translation Online – is a partially EU-funded commercialization project which specializes in the automatic retrieval and translation of patent documents. At the core of the PLuTO framework is a machine translation (MT) engine through which web-based translation services are offered. The fully integrated PLuTO architecture includes a translation engine coupling MT with translation memories (TM), and a patent search and retrieval engine. In this paper, we first describe the motivating factors behind the provision of such a service. Following this, we give an overview of the PLuTO framework as a whole, with particular emphasis on the MT components, and provide a real world use case scenario in which PLuTO MT services are ex- ploited.

In this paper, we give a description of the machine translation (MT) system developed at DCU that was used for our third participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2008). In this participation, we focus on various techniques for word and phrase alignment to improve system quality. Specifically, we try out our word packing and syntax-enhanced word alignment techniques for the Chinese–English task and for the English–Chinese task for the first time. For all translation tasks except Arabic–English, we exploit linguistically motivated bilingual phrase pairs extracted from parallel treebanks. We smooth our translation tables with out-of-domain word translations for the Arabic–English and Chinese–English tasks in order to solve the problem of the high number of out of vocabulary items. We also carried out experiments combining both in-domain and out-of-domain data to improve system performance and, finally, we deploy a majority voting procedure combining a language model-based method and a translation-based method for case and punctuation restoration. We participated in all the translation tasks and translated both the single-best ASR hypotheses and the correct recognition results. The translation results confirm that our new word and phrase alignment techniques are often helpful in improving translation quality, and the data combination method we proposed can significantly improve system performance.

We consider the value of replacing and/or combining string-basedmethods with syntax-based methods for phrase-based statistical machine translation (PBSMT), and we also consider the relative merits of using constituency-annotated vs. dependency-annotated training data. We automatically derive two subtree-aligned treebanks, dependency-based and constituency-based, from a parallel English–French corpus and extract syntactically motivated word- and phrase-pairs. We automatically measure PB-SMT quality. The results show that combining string-based and syntax-based word- and phrase-pairs can improve translation quality irrespective of the type of syntactic annotation. Furthermore, using dependency annotation yields greater translation quality than constituency annotation for PB-SMT.