scholarly journals MACHINE TRANSLATION IN THE FIELD OF LAW: A STUDY OF THE TRANSLATION OF ITALIAN LEGAL TEXTS INTO GERMAN

2019 ◽  
Vol 37 ◽  
pp. 117-153
Author(s):  
Eva WIESMANN
Keyword(s):  
Machine Translation ◽  
Translation System ◽  
Legal Language ◽  
Legal Text ◽  
Raising Awareness ◽  
Legal Texts ◽  
Different Types ◽  
Machine Translation System ◽  
Translation Pedagogy ◽  
Legal Expertise

With the advent of the neural paradigm, machine translation has made another leap in quality. As a result, its use by trainee translators has increased considerably, which cannot be disregarded in translation pedagogy. However, since legal texts have features that pose major challenges to machine translation, the question arises as to what extent machine translation is now capable of translating legal texts or at least certain types of legal text into another legal language well enough so that the post-editing effort is limited, and, consequently, whether a targeted use in translation pedagogy can be considered. In order to answer this question, DeepL Translator, a machine translation system, and MateCat, a CAT system that integrates machine translation, were tested. The test, undertaken at different times and without specific translation memories, provided for the translation of several legal texts of different types utilising both systems, and was followed by systematisation of errors and evaluation of translation results. The evaluation was carried out according to the following criteria: 1) comprehensibility and meaningfulness of the target text; and 2) correspondence between source and target text in consideration of the specific translation situation. Overall, the results are considered insufficient to give post-editing of machine-translated legal texts a bigger place in translation pedagogy. As the evaluation of the correspondence between source and target text was fundamentally worse than with regard to the meaningfulness of the target text, translation pedagogy should respond by raising awareness about differences between machine translation output and human translation in this field, and by improving translation approach and strengthening legal expertise.

scholarly journals Development of Telugu-Tamil Transfer-Based Machine Translation System: An Improvization Using Divergence Index

2019 ◽  
Vol 28 (3) ◽  
pp. 493-504
Author(s):  
Parameswari Krishnamurthy
Keyword(s):  
Machine Translation ◽  
Evaluation Method ◽  
Translation System ◽  
High Quality ◽  
Linguistic Differences ◽  
Arduous Task ◽  
Different Types ◽  
Machine Translation System ◽  
The Given ◽  
Language Pair

Abstract Building an automatic, high-quality, robust machine translation (MT) system is a fascinating yet an arduous task, as one of the major difficulties lies in cross-linguistic differences or divergences between languages at various levels. The existence of translation divergence precludes straightforward mapping in the MT system. An increase in the number of divergences also increases the complexity, especially in linguistically motivated transfer-based MT systems. This paper discusses the development of Telugu-Tamil transfer-based MT and how a divergence index (DI) is built to quantify the number of parametric variations between languages in order to improve the success rate of MT. The DI facilitates MT in proposing where to put efforts for the given language pair to attain better and faster results. In addition, handling strategies of different types of divergences in a transfer-based approach to MT are discussed. The paper also includes the evaluation method and how an improvization takes place with the application of DI in MT.

A Minimal Recursion Semantic Analysis of Locatives

2009 ◽  
Vol 35 (2) ◽  
pp. 229-270 ◽  
Author(s):  
Fredrik Jørgensen ◽  
Jan Tore Lønning
Keyword(s):  
Machine Translation ◽  
Semantic Analysis ◽  
Semantic Representation ◽  
Lexical Entry ◽  
Translation System ◽  
Referring Expressions ◽  
Different Types ◽  
Machine Translation System ◽  
Pilot Implementation ◽  
Syntactic Context

The article describes a pilot implementation of a grammar containing different types of locative PPs. In particular, we investigate the distinction between static and directional locatives, and between different types of directional locatives. Locatives may act as modifiers as well as referring expressions depending on the syntactic context. We handle this with a single lexical entry. The implementation is of Norwegian locatives, but English locatives are both discussed and compared to Norwegian locatives. The semantic analysis is based on a proposal by Markus Kracht (2002), and we show how this analysis can be incorporated into Minimal Recursion Semantics (MRS) (Copestake et al. 2005). We discuss how the resulting system may be applied in a transfer-based machine translation system, and how we can map from a shallow MRS representation to a deeper semantic representation.

Translation Divergence Patterns Handling in English to Urdu Machine Translation

2018 ◽  
Vol 27 (05) ◽  
pp. 1850017
Author(s):  
Shahnawaz Khan ◽  
Usama Mir ◽  
Salam S. Shreem ◽  
Sultan Alamri
Keyword(s):  
Machine Translation ◽  
Translation System ◽  
Computational Algorithms ◽  
Different Types ◽  
Machine Translation System

English to Urdu machine translation is still in its infancy. This study illustrates various types of translation divergences and their implication in English to Urdu machine translation. The divergence in English to Urdu machine translation can be thought as representing the translation divergences between Subject-Verb-Object (SVO) class languages to Subject-Object-Verb (SOV) class languages. This study discusses the different types of divergences in English to Urdu machine translation and presents novel computational algorithms to detect and to resolve these divergences in English to Urdu Machine Translation. These algorithms for detection of divergence have been implemented in English to Urdu Machine Translation system, and results have been presented in this paper. The work introduced here is the only one, to the best of our knowledge, which automatically detects and resolves divergences in English to Urdu machine translation.

English-Dogri Translation System using MOSES

2016 ◽  
Vol 1 (1) ◽  
pp. 45-49
Author(s):  
Avinash Singh ◽  
Asmeet Kour ◽  
Shubhnandan S. Jamwal
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpus ◽  
English System ◽  
Machine Translation System ◽  
Translation Machine ◽  
Language Pair

The objective behind this paper is to analyze the English-Dogri parallel corpus translation. Machine translation is the translation from one language into another language. Machine translation is the biggest application of the Natural Language Processing (NLP). Moses is statistical machine translation system allow to train translation models for any language pair. We have developed translation system using Statistical based approach which helps in translating English to Dogri and vice versa. The parallel corpus consists of 98,973 sentences. The system gives accuracy of 80% in translating English to Dogri and the system gives accuracy of 87% in translating Dogri to English system.

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