An English-to-Turkish interlingual MT system

This paper describes the integration of a Turkish generation system with the KANT knowledge-based machine translation system to produce a prototype English-Turkish interlingua-based machine translation system. These two independently constructed systems were successfully integrated within a period of two months, through development of a module which maps KANT interlingua expressions to Turkish syntactic structures. The combined system is able to translate completely and correctly 44 of 52 benchmark sentences in the domain of broadcast news captions. This study is the first known application of knowledge-based machine translation from English to Turkish, and our initial results show promise for future development. © Springer-Verlag Berlin Heidelberg 1998

Moving Boundaries in Translation Studies

Translation is in motion. Both translation practice and translation studies (TS) have seen considerable innovation in recent decades, and we are currently witnessing a wealth of new approaches and concepts, some of which refect new translation phenomena, whereas others mirror new scholarly foci. Volunteer translation, crowdsourcing, virtual translator networks, transediting, and translanguaging are only some examples of practices and notions that are emerging on the scene alongside a renewed focus on well-established concepts that have traditionally been considered peripheral to the practice and study of translation: intralingual and intersemiotic translation are cases in point. At the same time, technological innovation and global developments such as the spread of English as a lingua franca are affecting wide areas of translation and, with it, translation studies. These trends are currently pushing or even crossing our traditional understandings of translation (studies) and its boundaries. The question is: how to deal with these developments? Some areas of the translation profession seem to respond by widening its borders, adding new practices such as technical writing, localisation, transcreation, or post-editing to their job portfolios, whereas others seem to be closing ranks. The same trend can be observed in the academic discipline: some branches of translation studies are eager to embrace all new developments under the TS umbrella, whereas others tend to dismiss (some of) them as irrelevant or as merely refecting new names for age-old practices. Translation is in motion. Technological developments, digitalisation and globalisation are among the many factors affecting and changing translation and, with it, translation studies. Moving Boundaries in Translation Studies offers a bird’s-eye view of recent developments and discusses their implications for the boundaries of the discipline. With 15 chapters written by leading translation scholars from around the world, the book analyses new translation phenomena, new practices and tools, new forms of organisation, new concepts and names as well as new scholarly approaches and methods. This is key reading for scholars, researchers and advanced students of translation and interpreting studies. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 licens

Investigating the Interlingual Errors in Students’ Essay: The Case of Error Classification

The purpose of the present study was to identify the most frequently observed interlingual errors in the corpus under the study and the possible relationship between the gender and the type of interlingual errors. The results of this study revealed that the most frequently observed interlingual errors in the corpus under the study are spelling, word order, and punctuation errors and there is significant relationship between the gender and the type of interlingual errors (L1 interference and spelling, word order, and punctuation errors) on the basis of the obtained results for EFL learners. Accordingly based on the results it can be concluded that there is a relationship between interlingual errors and gender since the findings revealed that first, female participants make more spelling, word order, and punctuation errors (65%) by overlooking the writing system rules in the target language compared to the male participants (35%). Second, the results of the study confirm that female participants make more literal translation errors (55%) by translating his first language sentence or idiomatic expression in to the target language word by word compared to the male participants (45%)

Moving Boundaries in Translation Studies

Translation is in motion. Both translation practice and translation studies (TS) have seen considerable innovation in recent decades, and we are currently witnessing a wealth of new approaches and concepts, some of which refect new translation phenomena, whereas others mirror new scholarly foci. Volunteer translation, crowdsourcing, virtual translator networks, transediting, and translanguaging are only some examples of practices and notions that are emerging on the scene alongside a renewed focus on well-established concepts that have traditionally been considered peripheral to the practice and study of translation: intralingual and intersemiotic translation are cases in point. At the same time, technological innovation and global developments such as the spread of English as a lingua franca are affecting wide areas of translation and, with it, translation studies. These trends are currently pushing or even crossing our traditional understandings of translation (studies) and its boundaries. The question is: how to deal with these developments? Some areas of the translation profession seem to respond by widening its borders, adding new practices such as technical writing, localisation, transcreation, or post-editing to their job portfolios, whereas others seem to be closing ranks. The same trend can be observed in the academic discipline: some branches of translation studies are eager to embrace all new developments under the TS umbrella, whereas others tend to dismiss (some of) them as irrelevant or as merely refecting new names for age-old practices. Translation is in motion. Technological developments, digitalisation and globalisation are among the many factors affecting and changing translation and, with it, translation studies. Moving Boundaries in Translation Studies offers a bird’s-eye view of recent developments and discusses their implications for the boundaries of the discipline. With 15 chapters written by leading translation scholars from around the world, the book analyses new translation phenomena, new practices and tools, new forms of organisation, new concepts and names as well as new scholarly approaches and methods. This is key reading for scholars, researchers and advanced students of translation and interpreting studies. The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 licens

Toward Semantic Machine Translation

This thesis presents a novel approach to interlingual machine translation using λ-calculus expressions as an intermediate representation. It investigates and extends existing algorithms which learn a combinatorial category grammar for semantic parsing, and introduces two new algorithms for generation out of logical forms inspired by that semantic parser. The results of a set of new experiments for generation and parsing are described, as well as an evaluation of the performance of a semantic translation system created by joining the semantic parser and generator together. Experimental results demonstrate that under certain conditions, this semantic model achieves better performance than a standard phrase-based statistical MT system in both an automated evaluation of translation output and a manual evaluation of adequacy and fluency

A hybrid machine translation system from Turkish to English

Machine Translation (MT) is the process of automatically transforming a text in one natural language into an equivalent text in another natural language, so that the meaning is preserved. Even though it is one of the first applications of computers, state-of-the-art systems are far from being an alternative to human translators. Nevertheless, the demand for translation is increasing and the supply of human translators is not enough to satisfy this demand. International corporations, organizations, universities, and many others need to deal with different languages in everyday life, which creates a need for translation. Therefore, MT systems are needed to reduce the effort and cost of translation, either by doing some of the translations, or by assisting human translators in some ways. In this work, we introduce a hybrid machine translation system from Turkish to English, by combining two different approaches to MT. Transfer-based approaches have been successful at expressing the structural differences between the source and target languages, while statistical approaches have been useful at extracting relevant probabilistic models from huge amounts of parallel text that would explain the translation process. The hybrid approach transfers a Turkish sentence to all of its possible English translations, using a set of manually written transfer rules. Then, it uses a probabilistic language model to pick the most probable translation out of this set. We have evaluated our system on a test set of Turkish sentences, and compared the results to reference translations

Byte-based neural machine translation

This paper presents experiments compar- ing character-based and byte-based neural machine translation systems. The main motivation of the byte-based neural ma- chine translation system is to build multi- lingual neural machine translation systems that can share the same vocabulary. We compare the performance of both systems in several language pairs and we see that the performance in test is similar for most language pairs while the training time is slightly reduced in the case of byte-based neural machine translation.Postprint (author's final draft

Abstract syntax as interlingua: Scaling up the grammatical framework from controlled languages to robust pipelines

Syntax is an interlingual representation used in compilers. Grammatical Framework (GF) applies the abstract syntax idea to natural languages. The development of GF started in 1998, first as a tool for controlled language implementations, where it has gained an established position in both academic and commercial projects. GF provides grammar resources for over 40 languages, enabling accurate generation and translation, as well as grammar engineering tools and components for mobile and Web applications. On the research side, the focus in the last ten years has been on scaling up GF to wide-coverage language processing. The concept of abstract syntax offers a unified view on many other approaches: Universal Dependencies, WordNets, FrameNets, Construction Grammars, and Abstract Meaning Representations. This makes it possible for GF to utilize data from the other approaches and to build robust pipelines. In return, GF can contribute to data-driven approaches by methods to transfer resources from one language to others, to augment data by rule-based generation, to check the consistency of hand-annotated corpora, and to pipe analyses into high-precision semantic back ends. This article gives an overview of the use of abstract syntax as interlingua through both established and emerging NLP applications involving GF