Design of computer aided translation system for English communication language based on grey clustering evaluation

In order to facilitate communication and communication, this paper studies the optimization of the current computer-aided translation system, and proposed a design method of English communication language computer-aided translation system based on grey clustering evaluation. By optimizing the hardware configuration and algorithm function keys of the system, the English translation mechanism of multilanguage interaction, the design idea of editing and modifying after English translation and knowledge database management are realized, and the system function framework was constructed, including the system transceiver unit, automatic translation unit, manual correction unit, task management unit and memory management unit, the performance of task management unit and memory management unit is analyzed. On this basis, the specific work flow of the design system mainly includes the English translation service flow integrating multilanguage interaction and the project-based multilanguage interaction English translation service flow design, which realizes the English translation online assistance under the multilanguage interaction environment. The experimental results show that the design system has the advantages of high online translation speed, pronunciation success rate and multilanguage translation success rate.

Feedback Loops and Facilitation: Catalyzing Transformational Multi-Stakeholder Refugee Response Partnerships

Global policies such as the recent ‘Comprehensive Refugee Response Framework’ call for a profound transformation in refugee response. To this end, collaboration with non-traditional humanitarian actors, particularly the private sector has been advocated. The application of new multi-stakeholder partnerships that transcend traditional dyadic relationships have been commended by practitioners for their ability to create stable services and markets in refugee camps. However, the adaptation of multi-stakeholder partnership models to the novelties of refugee response and the dynamics among partners in these complex arrangements requires more attention. This paper explores how the creation and development of multi-stakeholder partnerships can maximize the transformational potential of collaboration for refugee response, ensure the stakeholder diversity needed to provide basic services on a stable basis, and provide a facilitation function that supports the partnership. Using an action-case methodology, the focus of the article is on the Alianza Shire, Spain’s first multi-stakeholder partnership for humanitarian action, which was established to provide energy to refugee camps and host communities in refugee camps in northern Ethiopia. Our findings suggest that i) the active participation of aid agencies in the co-creation process of a multi-stakeholder partnership may increase the transformational potential of refugee response, ii) feedback loops and the consolidation of internal learning are essential practices for the effective management of complex multi-stakeholder partnerships, and iii) the facilitator plays a critical and underexplored role in refugee response collaborative arrangements. In addition, sustainability-oriented university centers may possess a particular capacity for nurturing the transformational potential of multi-stakeholder refugee response partnerships by generating ‘safe spaces’ that foster trust-building, providing a cross-sector ‘translation’ service, and affording the legitimacy and expert knowledge required to conduct learning processes. We believe that the theoretical and practical implications of our research may contribute to the effective fulfilment of the Sustainable Development Goals, specially, SDG7 (Affordable and Clean Energy) and SDG17 (Partnership for the Goals).

The German EU Council Presidency Translator

This contribution describes the German EU Council Presidency Translator (EUC PT), a machine translation service created for the German EU Council Presidency in the second half of 2020, which is open to the general public. Following a series of earlier presidency translators, the German version exhibits important extensions and improvements. The German EUC PT is the first to integrate systems from commercial vendors, public services, and a research center, using a mix of custom and generic translation engines, and to introduce a new webpage translation widget. A further important feature is the close collaboration with human translators from the German ministries, who were provided with computer-assisted translation tool plugins integrating machine translation services into their daily work environments. Uptake by the public reflects a huge interest in the service, showing the need for breaking language barriers.

Multilingualism in Scholarly Communication

The results of the study and the survey conducted on behalf of the OPERAS-P project (Task “Innovative Models of Bibliodiversity in Scholarly Publications”) were concluded in June 2021 and sought to achieve the following objectives: to prepare a theoretical background to discuss the use of multilingualism in scholarly communication; to identify, analyse, and understand the innovative dynamics of working practices and knowledge-sharing within linguistically diverse scholarly contexts and research networks; to identify and analyse the motivations behind these practices (questionnaires/focus groups – how tools may answer to needs); to formulate recommendations/guidelines for OPERAS and other stakeholders regarding the future implementation of a service aimed at enhancing multilingualism; to prepare the conceptual design of a platform prototype for a shared translation service at the scholarly communication level (involving publishers, translators, and researchers). This presentation approaches the most important stages of the work done, as well as the main findings and the challenges they pose for future developments and their implementation.

Reinforced NMT for Sentiment and Content Preservation in Low-resource Scenario

The preservation of domain knowledge from source to the target is crucial in any translation workflows. Hence, translation service providers that use machine translation (MT) in production could reasonably expect that the translation process should transfer both the underlying pragmatics and the semantics of the source-side sentences into the target language. However, recent studies suggest that the MT systems often fail to preserve such crucial information (e.g., sentiment, emotion, gender traits) embedded in the source text in the target. In this context, the raw automatic translations are often directly fed to other natural language processing (NLP) applications (e.g., sentiment classifier) in a cross-lingual platform. Hence, the loss of such crucial information during the translation could negatively affect the performance of such downstream NLP tasks that heavily rely on the output of the MT systems. In our current research, we carefully balance both the sides (i.e., sentiment and semantics) during translation, by controlling a global-attention-based neural MT (NMT), to generate translations that encode the underlying sentiment of a source sentence while preserving its non-opinionated semantic content. Toward this, we use a state-of-the-art reinforcement learning method, namely, actor-critic , that includes a novel reward combination module, to fine-tune the NMT system so that it learns to generate translations that are best suited for a downstream task, viz. sentiment classification while ensuring the source-side semantics is intact in the process. Experimental results for Hindi–English language pair show that our proposed method significantly improves the performance of the sentiment classifier and alongside results in an improved NMT system.

A self-efficacy scale for measuring student progress in translation company simulations

Applying factor analysis on survey data, this paper develops a concise scale of translation service provision self-efficacy aimed for diagnosing learning needs and assessing progress in pedagogical translation company simulations. First, a model of translation service provision activities based on the translation service provision standard ISO 17100 and a business process model of translation service is constructed and operationalized as a draft scale. The draft scale is then tested in an international survey (n = 380) conducted in connection with translation company simulation courses in university-level translator education. Exploratory factor analysis is used to identify dimensions and adequate items for a concise scale that comprises two four-item subscales: a project management self-efficacy subscale and a translation-production self-efficacy subscale. The scale is validated through confirmatory factor analysis. It is expected to be useful as a light-weight measurement instrument for frequent testing or as a compact part of more extensive scales.

Local Translation Services for Neglected Languages

Taking advantage of computationally lightweight, but high-quality translators prompt consideration of new applications that address neglected languages. For projects with protected or personal data, translators for less popular or low-resource languages require specific compliance checks before posting to a public translation API. In these cases, locally run translators can render reasonable, cost-effective solutions if done with an army of offline, smallscale pair translators. Like handling a specialist’s dialect, this research illustrates translating two historically interesting, but obfuscated languages: 1) hacker-speak (“l33t”) and 2) reverse (or “mirror”) writing as practiced by Leonardo da Vinci. The work generalizes a deep learning architecture to translatable variants of hacker-speak with lite, medium, and hard vocabularies. The original contribution highlights a fluent translator of hacker-speak in under 50 megabytes and demonstrates a companion text generator for augmenting future datasets with greater than a million bilingual sentence pairs. A primary motivation stems from the need to understand and archive the evolution of the international computer community, one that continuously enhances their talent for speaking openly but in hidden contexts. This training of bilingual sentences supports deep learning models using a long short-term memory, recurrent neural network (LSTM-RNN). It extends previous work demonstrating an English-to-foreign translation service built from as little as 10,000 bilingual sentence pairs. This work further solves the equivalent translation problem in twenty-six additional (non-obfuscated) languages and rank orders those models and their proficiency quantitatively with Italian as the most successful and Mandarin Chinese as the most challenging. For neglected languages, the method prototypes novel services for smaller niche translations such as Kabyle (Algerian dialect) which covers between 5-7 million speakers but one which for most enterprise translators, has not yet reached development. One anticipates the extension of this approach to other important dialects, such as translating technical (medical or legal) jargon and processing health records or handling many of the dialects collected from specialized domains (mixed languages like “Spanglish”, acronym-laden Twitter feeds, or urban slang).

Controlled Multilingual Thesauri for Kazakh Industry-Specific Terms

This article discusses the practical issues of compiling controlled multilingual thesauri for the purposes of industry-specific translation (IST). In the multilingual, transnational and globally connected Kazakhstan, IST is a much-needed translation service. IST is an interdisciplinary field between terminology, computational linguistics, translation theory and practice. Most of the professional guides, dictionaries and glossaries are systemized in alphabetical order and contain multiple variants for the terms searched. Therefore, there is an urgent need to create a systemized controlled multilingual thesaurus of industry-specific Kazakh, English and Russian terms in order to provide multilingual users with an interoperable and relevant term base. Controlled multilingual thesauri for industry-specific terms are the most effective tools for describing individual subject areas. They are designed to promote communication and interaction among professionals, translators and all Automated Information System users of specific fields irrespective of their location and health conditions. Unlike traditional dictionaries, controlled thesauri allow users to identify the meaning with the help of definitions and translations, relations of terms with other concepts, and broader and narrower terms. The purpose of this research is to unify and systematize industry-specific terms in Kazakh, to provide Russian and English equivalents, and to classify the terms into essential rubrics and subjects. Based on the Zthes data scheme to create a controlled multilingual thesaurus of industryspecific terms, the major rubrics have been formulated, and about 10,000 Kazakh mining and metal terms approved by the Terminological Committee of Kazakhstan have been structured.