Domain adaptation strategies in statistical machine translation: a brief overview

© Cambridge University Press, 2015.Statistical machine translation (SMT) is gaining interest given that it can easily be adapted to any pair of languages. One of the main challenges in SMT is domain adaptation because the performance in translation drops when testing conditions deviate from training conditions. Many research works are arising to face this challenge. Research is focused on trying to exploit all kinds of material, if available. This paper provides an overview of research, which copes with the domain adaptation challenge in SMT.Peer ReviewedPostprint (author's final draft

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

PLuTO: MT for online patent translation

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 exploited

Cloud service localisation

The essence of cloud computing is the provision of software and hardware services to a range of users in dierent locations. The aim of cloud service localisation is to facilitate the internationalisation and localisation of cloud services by allowing their adaption to dierent locales. We address the lingual localisation by providing service-level language translation techniques to adopt services to dierent languages and regulatory localisation by providing standards-based mappings to achieve regulatory compliance with regionally varying laws, standards and regulations. The aim is to support and enforce the explicit modelling of aspects particularly relevant to localisation and runtime support consisting of tools and middleware services to automating the deployment based on models of locales, driven by the two localisation dimensions. We focus here on an ontology-based conceptual information model that integrates locale specication in a coherent way

Domain adaptation : Retraining NMT with translation memories

The topic of this thesis is domain adaptation of an NMT system by retraining it with translation memories. The translation memory used in the experiments is the EMEA corpus that consists of medical texts – mostly package leaflets. The NMT system used in the experiments is OpenNMT because it is completely free and easy to use. The goal of this thesis is to find out how an NMT system can be adapted to a special domain, and if the translation quality improves after domain adaptation. The original plan was to continue training the pretrained model of OpenNMT with EMEA data, but this is not possible. Therefore, it is necessary to train a new baseline model with the same data as the pretrained model was trained with. After this two domain adaptation methods are tested: continuation training with EMEA data and continuation training with unknown terms. In the manual evaluation, it turned out that domain adaptation with unknown terms worsens the translation quality drastically because all sentences are translated as single words. This method is only suitable for translating wordlists because it improved the translation of unknown terms. Domain adaptation with EMEA data, for the other hand, improves the translation quality significantly. The EMEA-retrained system translates long sentences and medical terms much better than the pretrained and the baseline models. Long and complicated terms are still difficult to translate but the EMEA-retrained model makes fewer errors than the other models. The evaluation metrics used for automatic evaluation are BLEU and LeBLEU. BLEU is stricter than LeBLEU. The results are similar as in the manual evaluation: The EMEA-retrained model translates medical texts much better than the other models, and the translation quality of the UNK-retrained model is the worst of all. It can be presumed that an NMT system needs contextual information so that it learns to translate terms and long sentences without transforming the text into a wordlist without sentences. In addition, it seems that long terms are translated in smaller pieces so that the NMT system possibly translates some pieces wrong, which results in that the whole term is wrong

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)

Language technologies for a multilingual Europe

This volume of the series “Translation and Multilingual Natural Language Processing” includes most of the papers presented at the Workshop “Language Technology for a Multilingual Europe”, held at the University of Hamburg on September 27, 2011 in the framework of the conference GSCL 2011 with the topic “Multilingual Resources and Multilingual Applications”, along with several additional contributions. In addition to an overview article on Machine Translation and two contributions on the European initiatives META-NET and Multilingual Web, the volume includes six full research articles. Our intention with this workshop was to bring together various groups concerned with the umbrella topics of multilingualism and language technology, especially multilingual technologies. This encompassed, on the one hand, representatives from research and development in the field of language technologies, and, on the other hand, users from diverse areas such as, among others, industry, administration and funding agencies. The Workshop “Language Technology for a Multilingual Europe” was co-organised by the two GSCL working groups “Text Technology” and “Machine Translation” (http://gscl.info) as well as by META-NET (http://www.meta-net.eu)