scholarly journals Editing Taiwan divination Verses with controlled Language Strategies: Machine-Translation-Mediated Effective Communication

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Chung-ling Shih
Keyword(s):  
Machine Translation ◽  
Effective Communication ◽  
Language Strategies ◽  
Controlled Language

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.

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.

Language-based communication strategies that support person-centered communication with persons with dementia

2015 ◽  
Vol 27 (10) ◽  
pp. 1707-1718 ◽  
Author(s):  
Marie Y. Savundranayagam ◽  
Kelsey Moore-Nielsen
Keyword(s):  
Similar Pattern ◽  
Communication Strategies ◽  
Effective Communication ◽  
Routine Care ◽  
Specific Language ◽  
Support Person ◽  
Language Strategies

ABSTRACTBackground:There are many recommended language-based strategies for effective communication with persons with dementia. What is unknown is whether effective language-based strategies are also person centered. Accordingly, the objective of this study was to examine whether language-based strategies for effective communication with persons with dementia overlapped with the following indicators of person-centered communication: recognition, negotiation, facilitation, and validation.Methods:Conversations (N = 46) between staff-resident dyads were audio-recorded during routine care tasks over 12 weeks. Staff utterances were coded twice, using language-based and person-centered categories. There were 21 language-based categories and 4 person-centered categories.Results:There were 5,800 utterances transcribed: 2,409 without indicators, 1,699 coded as language or person centered, and 1,692 overlapping utterances. For recognition, 26% of utterances were greetings, 21% were affirmations, 13% were questions (yes/no and open-ended), and 15% involved rephrasing. Questions (yes/no, choice, and open-ended) comprised 74% of utterances that were coded as negotiation. A similar pattern was observed for utterances coded as facilitation where 51% of utterances coded as facilitation were yes/no questions, open-ended questions, and choice questions. However, 21% of facilitative utterances were affirmations and 13% involved rephrasing. Finally, 89% of utterances coded as validation were affirmations.Conclusions:The findings identify specific language-based strategies that support person-centered communication. However, between 1 and 4, out of a possible 21 language-based strategies, overlapped with at least 10% of utterances coded as each person-centered indicator. This finding suggests that staff need training to use more diverse language strategies that support personhood of residents with dementia.

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.

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