Leveraging bilingual terminology to improve machine translation in a CAT environment

This work focuses on the extraction and integration of automatically aligned bilingual terminology into a Statistical Machine Translation (SMT) system in a Computer Aided Translation (CAT) scenario. We evaluate the proposed framework that, taking as input a small set of parallel documents, gathers domain-specific bilingual terms and injects them into an SMT system to enhance translation quality. Therefore, we investigate several strategies to extract and align terminology across languages and to integrate it in an SMT system. We compare two terminology injection methods that can be easily used at run-time without altering the normal activity of an SMT system: XML markup and cache-based model. We test the cache-based model on two different domains (information technology and medical) in English, Italian and German, showing significant improvements ranging from 2.23 to 6.78 BLEU points over a baseline SMT system and from 0.05 to 3.03 compared to the widely-used XML markup approach

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Exploiting Data-Driven Hybrid Approaches to Translation in the EXPERT Project

Technologies have transformed the way we work, and this is also applicable to the translation industry. In the past thirty to thirty-five years, professional translators have experienced an increased technification of their work. Barely thirty years ago, a professional translator would not have received a translation assignment attached to an e-mail or via an FTP and yet, for the younger generation of professional translators, receiving an assignment by electronic means is the only reality they know. In addition, as pointed out in several works such as Folaron (2010) and Kenny (2011), professional translators now have a myriad of tools available to use in the translation process.Published versio

TectoMT – a deep-­linguistic core of the combined Chimera MT system

Chimera is a machine translation system that combines the TectoMT deep-linguistic core with phrase-based MT system Moses. For English–Czech pair it also uses the Depfix post-correction system. All the components run on Unix/Linux platform and are open source (available from Perl repository CPAN and the LINDAT/CLARIN repository). The main website is https://ufal.mff.cuni.cz/tectomt. The development is currently supported by the QTLeap 7th FP project (http://qtleap.eu)

Machine translation for institutional academic texts: Output quality, terminology translation and post-editor trust

The present work is a feasibility study on the application of Machine Translation (MT) to institutional academic texts, specifically course catalogues, for Italian-English and German-English. The first research question of this work focuses on the feasibility of profitably applying MT to such texts. Since the benefits of a good quality MT might be counteracted by preconceptions of translators towards the output, the second research question examines translator trainees' trust towards an MT output as compared to a human translation (HT). Training and test sets are created for both language combinations in the institutional academic domain. MT systems used are ModernMT and Google Translate. Overall evaluations of the output quality are carried out using automatic metrics. Results show that applying neural MT to institutional academic texts can be beneficial even when bilingual data are not available. When small amounts of sentence pairs become available, MT quality improves. Then, a gold standard data set with manual annotations of terminology (MAGMATic) is created and used for an evaluation of the output focused on terminology translation. The gold standard was publicly released to stimulate research on terminology assessment. The assessment proves that domain-adaptation improves the quality of term translation. To conclude, a method to measure trust in a post-editing task is proposed and results regarding translator trainees trust towards MT are outlined. All participants are asked to work on the same text. Half of them is told that it is an MT output to be post-edited, and the other half that it is a HT needing revision. Results prove that there is no statistically significant difference between post-editing and HT revision in terms of number of edits and temporal effort. Results thus suggest that a new generation of translators that received training on MT and post-editing is not influenced by preconceptions against MT

Apps-based Machine Translation on Smart Media Devices - A Review

Machine Translation Systems are part of Natural Language Processing (NLP) that makes communication possible among people using their own native language through computer and smart media devices. This paper describes recent progress in language dictionaries and machine translation commonly used for communications and social interaction among people or Internet users worldwide who speak different languages. Problems of accuracy and quality related to computer translation systems encountered in web & Apps-based translation are described and discussed. Possible programming solutions to the problems are also put forward to create software tools that are able to analyze and synthesize language intelligently based on semantic representation of sentences and phrases. Challenges and problems on Apps-based machine translation on smart devices towards AI, NLP, smart learning and understanding still remain until now, and need to be addressed and solved through collaboration between computational linguists and computer scientists