In search of the recurrent units of translation

Translation memory systems aim to reuse previously translated texts. Be-cause the operational unit of the first-generation translation memory sys-tems is the sentence, such systems are only useful for text types in which full-sentence repetition frequently occurs. Second-generation sub-sentential translation memory systems try to remedy this problem by providing addi-tional translation suggestions for sub-sentential chunks. In this paper, we compare the performance of a sentence-based translation memory system (SDL Trados Translator’s Workbench) with a sub-sentential translation memory system (Similis) on different text types. We demonstrate that some text types (in this case, journalistic texts) are not suited to be translated by means of a translation memory system. We show that Similis offers useful additional translation suggestions for terminology and frequent multiword expressions.

The effect of Translation Memory tools in translated Web texts: Evidence from a comparative product-based study

Translation Memory tools have been widely promoted in terms of increased productivity, quality and consistency, while translation scholars have ar-gued that in some cases they might produce the opposite effect. This paper investigates these two related claims through a corpus-based contrastive analysis of 40,000 original and localized Web pages in Spanish. Given that all Web texts are localized using TM tools, the claim of increased quality and consistency is analyzed in contrast with Web texts spontaneously pro-duced in Spanish. The results of the contrastive analysis indicate that local-ized texts tend to replicate source text structures and show higher numbers of inconsistencies at the lexical, syntactic and typographic levels than non-translated Web sites. These findings are associated with lower levels of quality in localized texts as compared to non-translated or spontaneously produced texts.

La constitución de una memoria terminológica: elaboración de terminologías bilingües en programas de memoria de traducción

Computer-aided translation systems (CAT) based on Translation Memories (TM) are a widely diffused technology that uses database and code-protection features to improve the quality, efficiency and consistency of the human translation process. These systems basically consist of a textual database in which each source sentence of a translation is stored together with the target sentence (this is called a translation memory “unit”). New and changed translation proposals will then be stored in the database for future use. This textual database – the kernel of the system – is combined with a terminological database (TDB), which is used by translators to store independently, terminological equivalences or translation units of particular value.In this paper the authors outline a first draft of a methodology that describes the preparation of a bilingual terminology from – and within – TM applications. The bilingual corpus produced is called the ‘terminological memory’ of the translator.

Comparative Evaluation of Translation Memory (TM) and Machine Translation (MT) Systems in Translation between Arabic and English

In general, advances in translation technology tools have enhanced translation quality significantly. Unfortunately, however, it seems that this is not the case for all language pairs. A concern arises when the users of translation tools want to work between different language families such as Arabic and English. The main problems facing Arabic<>English translation tools lie in Arabic’s characteristic free word order, richness of word inflection – including orthographic ambiguity – and optionality of diacritics, in addition to a lack of data resources. The aim of this study is to compare the performance of translation memory (TM) and machine translation (MT) systems in translating between Arabic and English.The research evaluates the two systems based on specific criteria relating to needs and expected results. The first part of the thesis evaluates the performance of a set of well-known TM systems when retrieving a segment of text that includes an Arabic linguistic feature. As it is widely known that TM matching metrics are based solely on the use of edit distance string measurements, it was expected that the aforementioned issues would lead to a low match percentage. The second part of the thesis evaluates multiple MT systems that use the mainstream neural machine translation (NMT) approach to translation quality. Due to a lack of training data resources and its rich morphology, it was anticipated that Arabic features would reduce the translation quality of this corpus-based approach. The systems’ output was evaluated using both automatic evaluation metrics including BLEU and hLEPOR, and TAUS human quality ranking criteria for adequacy and fluency.The study employed a black-box testing methodology to experimentally examine the TM systems through a test suite instrument and also to translate Arabic English sentences to collect the MT systems’ output. A translation threshold was used to evaluate the fuzzy matches of TM systems, while an online survey was used to collect participants’ responses to the quality of MT system’s output. The experiments’ input of both systems was extracted from Arabic<>English corpora, which was examined by means of quantitative data analysis. The results show that, when retrieving translations, the current TM matching metrics are unable to recognise Arabic features and score them appropriately. In terms of automatic translation, MT produced good results for adequacy, especially when translating from Arabic to English, but the systems’ output appeared to need post-editing for fluency. Moreover, when retrievingfrom Arabic, it was found that short sentences were handled much better by MT than by TM. The findings may be given as recommendations to software developers.

Quality Assurance in Computer-Assisted Translation in Business Environments

Increased use of computer-assisted translation (CAT) technology in business settings with augmented amounts of tasks, collaborative work, and short deadlines give rise to errors and the need for quality assurance (QA). The research has three operational aims: 1) methodological framework for QA analysis, 2) comparative evaluation of four QA tools, 3) to justify introduction of QA into CAT process. The research includes building of translation memory, terminology extraction, and creation of terminology base. Error categorization is conducted by multidimensional quality (MQM) framework. The level of mistake is calculated considering detected, false, and not detected errors. Weights are assigned to errors (minor, major, or critical), penalties are calculated, and quality estimation for translation memory is given. Results show that process is prone to errors due to differences in error detection, harmonization, and error counting. Data analysis of detected errors leads to further data-driven decisions related to the quality of output results and improved efficacy of translation business process.