How to treat GUI Options in IT Technical Texts for Authoring and Machine Translation

2009 ◽  
Vol 1 ◽  
pp. 40-59 ◽  
Author(s):  
Johann Roturier ◽  
Sabine Lehmann
Keyword(s):  
User Interface ◽  
Machine Translation ◽  
Technical Documentation ◽  
Technical Terms ◽  
Controlled Language

This paper focuses on one aspect of controlled authoring in a localization and Machine-Translation context: the treatment of GUI options, which abound in the procedural sections of IT technical documentation. GUI options are technical terms that refer to the Software User Interface. The length and complexity of GUI options is a major problem for numerous NLP tasks, including MT. GUI options which have not been identified by NLP applications typically lead to erroneous analyses of sentences. However, few authors have focused on the identification and tagging of GUI options in IT documentation. This paper delineates an approach based on a controlled language checker that benefits both the human authoring process and Machine Translation.

scholarly journals MT-ComparEval: Graphical evaluation interface for Machine Translation development

2015 ◽  
Vol 104 (1) ◽  
pp. 63-74 ◽  
Author(s):  
Ondřej Klejch ◽  
Eleftherios Avramidis ◽  
Aljoscha Burchardt ◽  
Martin Popel
Keyword(s):  
Comparative Analysis ◽  
User Interface ◽  
Machine Translation ◽  
Graphical User Interface ◽  
Statistical Significance ◽  
Web Based ◽  
Link Type ◽  
Evaluation Panel ◽  
N Gram

Abstract The tool described in this article has been designed to help MT developers by implementing a web-based graphical user interface that allows to systematically compare and evaluate various MT engines/experiments using comparative analysis via automatic measures and statistics. The evaluation panel provides graphs, tests for statistical significance and n-gram statistics. We also present a demo server http://wmt.ufal.cz with WMT14 and WMT15 translations.

scholarly journals Pedagogical technologies of machine translation skills forming on the example of bachelor students specializing in mechatronics and robotics at Don State Technical University

2021 ◽  
Vol 273 ◽  
pp. 12140
Author(s):  
Irina Gritsay ◽  
Liubov Vodyanitskaya
Keyword(s):  
Foreign Language ◽  
Machine Translation ◽  
Professional Training ◽  
Special Program ◽  
Technical Documentation ◽  
State Technical ◽  
Advantages And Disadvantages ◽  
Classroom Studies ◽  
Definition Of ◽  
And Robotics

In this paper the authors took into consideration professional training of specialists who can use technical documentation in a foreign language. The definition of machine translation, its value, main advantages and disadvantages were examined. This research showed the importance of machine translation and the need to train students of technical specialties in MT skills. The model of a special program for training students and its theoretical and practical parts were described. Based on the results of the error analysis, the error types were categorized into 3 categories. The results of the tests were analysed and shown in the tables. A significant decrease in the number of errors was noted. Based on the findings, pedagogical implications were discussed regarding how teachers can successfully and effectively incorporate MT into the classroom studies.

Sublanguages and Controlled Languages

2012 ◽  
Author(s):  
Richard I. Kittredge
Keyword(s):  
Natural Language ◽  
Machine Translation ◽  
Native Speakers ◽  
Language Generation ◽  
Original Language ◽  
Upper Limit ◽  
The Difference ◽  
Definition Of ◽  
Controlled Languages ◽  
Controlled Language

This article deals with the topic of sublanguage, the original language grammar subset, which informs various text outputs. Despite routine deviance from standard languages, quite often sublanguage grammatical patterns draw heavily from standard languages. Machine translation, database extraction from texts, and natural language generation are some ways of sublanguage processing. The definition of controlled language projects the difference between itself and sublangauge. The former is described as a restricted set of natural language, engineered to facilitate communication between expert native speakers and either non-expert natives or expert non-natives. However, the difference lies in the fact that controlled language is not a natural subset, unlike sublangauge. Unlike sublanguage that works like a general language in not restricting its sentences, controlled language sets an upper limit, typically around twenty-five. Contrast between controlled language and sublanguage assumes theoretical importance.

A classification approach for detecting cross-lingual biomedical term translations

2015 ◽  
Vol 23 (1) ◽  
pp. 31-51 ◽  
Author(s):  
H. HAKAMI ◽  
D. BOLLEGALA
Keyword(s):  
Machine Translation ◽  
Feature Space ◽  
Target Language ◽  
Average Precision ◽  
Common Features ◽  
Target Languages ◽  
Cross Lingual ◽  
Translation Accuracy ◽  
Translation Systems ◽  
Technical Terms

AbstractFinding translations for technical terms is an important problem in machine translation. In particular, in highly specialized domains such as biology or medicine, it is difficult to find bilingual experts to annotate sufficient cross-lingual texts in order to train machine translation systems. Moreover, new terms are constantly being generated in the biomedical community, which makes it difficult to keep the translation dictionaries up to date for all language pairs of interest. Given a biomedical term in one language (source language), we propose a method for detecting its translations in a different language (target language). Specifically, we train a binary classifier to determine whether two biomedical terms written in two languages are translations. Training such a classifier is often complicated due to the lack of common features between the source and target languages. We propose several feature space concatenation methods to successfully overcome this problem. Moreover, we study the effectiveness of contextual and character n-gram features for detecting term translations. Experiments conducted using a standard dataset for biomedical term translation show that the proposed method outperforms several competitive baseline methods in terms of mean average precision and top-k translation accuracy.

scholarly journals An in-depth analysis of the individual impact of controlled language rules on machine translation output: a mixed-methods approach

2021 ◽  
Author(s):  
Shaimaa Marzouk
Keyword(s):  
Mixed Methods ◽  
Machine Translation ◽  
Positive Impact ◽  
Neural System ◽  
System Level ◽  
Automatic Evaluation ◽  
Content Quality ◽  
Positive Effects ◽  
Depth Analysis ◽  
Controlled Language

AbstractExamining the general impact of Controlled Language (CL) rules in the context of Machine Translation (MT) has been an area of research for many years. The present study focuses on the following question: how do CL rules impact MT output individually? By analysing a German corpus-based test suite of technical texts that have been translated into English by different MT systems, this study endeavours to answer this question at different levels: the general impact of CL rules (rule- and system-independent), their impact at rule level (system-independent) as well as at rule and system level. The results of five MT systems are analysed and contrasted: a rule-based system, a statistical system, two differently constructed hybrid systems, and a neural system. For this, a mixed-methods triangulation approach that includes error annotation, human evaluation, and automatic evaluation was applied. The data was analysed both qualitatively and quantitatively in terms of CL influence on the following parameters: number and type of MT errors, style and content quality, and scores of two automatic evaluation metrics. In line with many studies, the results show a general positive impact of the applied CL rules on the MT output. However, at rule level, only four rules proved to have positive effects on the aforementioned parameters; three rules had negative effects on the parameters; and two rules did not show any significant impact. At rule and system level, the rules affected the MT systems differently, as expected. Rules that had a positive impact on earlier MT approaches did not show the same impact on the neural MT approach. Furthermore, neural MT delivered distinctly better results than earlier MT approaches, namely the highest error-free, style and content quality rates both before and after applying the rules, which indicates that neural MT offers a promising solution that no longer requires CL rules for improving the MT output.

scholarly journals Current Trends in Translation

2013 ◽  
Vol 2 (2) ◽  
pp. 41 ◽  
Author(s):  
Bruce Maylath
Keyword(s):  
Machine Translation ◽  
Global Economy ◽  
High Technology ◽  
Academic Programs ◽  
Technical Documentation ◽  
Early Stages ◽  
Current Trends ◽  
Cross Training ◽  
Technical Writers ◽  
Robert Bly

Driven by the growth of a global economy and developments in high technology, the process of creating and translating technical documentation has been evolving rapidly. In particular, machine translation (MT) has shown increasing capabilities of efficaciously accomplishing the early stages of the eight stages of translation identified years ago by Robert Bly. As a consequence, translators have learned to use MT as a tool to accelerate their work, but they have also grown wary of MT’s potential for replacing them. To ensure steady employment, some translators have begun cross-training as technical writers; correspondingly, a few technical writers have begun cross-training as translators, as the two professions appear to be undergoing a gradual trend of convergence. Academic programs are urged to respond to the evolving trends.

A Study Of Machine Translation Theories And Methods

2020 ◽  
Vol 2 (09) ◽  
pp. 665-670
Author(s):  
Guli I. Ergasheva ◽  
Zahriddin.X. Haitqulov
Keyword(s):  
Machine Translation ◽  
Information Exchange ◽  
Language Translation ◽  
Technical Documentation ◽  
Study Methods ◽  
Legal Documents ◽  
Statistical Mt ◽  
Methods And Techniques ◽  
History Of ◽  
Need For Information

The demand for language translation has greatly increased in recent times due to increasing cross-regional communication and the need for information exchange. Most material needs to be translated, including scientific and technical documentation, instruction manuals, legal documents, textbooks, publicity leaflets, newspaper reports etc. Some of this work is challenging and difficult but mostly it is tedious and repetitive and requires consistency and accuracy. It is becoming difficult for professional translators to meet the increasing demands of translation. In such a situation the machine translation can be used as a substitute.  This paper intends to study methods and techniques of Machine Translation (MT). Through the following points: History of MT, Statistical MT, Types of MT, and evaluation of MT.  

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