language-based transformation tools

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XMLTrans: a Java based XML Transformation Language for Structured Data

XMLTrans: a Java based XML Transformation Language for Structured Data

Edinburgh's Language Technology Group had produced a number of useful SGML/XML manipulation tools (LTG, 1999). Unfortunately none of these matched our specic needs. For instance, sgmltrans does not permit matching of complex expressions involving elements, text, and attributes. Another LTG tool, sgrpg is more powerful, but its control les have (in our opin- ion) a non-intuitive and complex syntax 3 .

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Graph and Model Transformation Tools for Model Migration

Graph and Model Transformation Tools for Model Migration

Ideally, empirical results on the comparison of trans- formation techniques are based on many case studies with many solutions per case study. In this paper, we contribute to that body of knowledge by providing 9 so- lutions to a common migration case study and by care- fully describing their properties. Ideally, we would have for our case study one solution for every design approach that is considered a best practice in transformation re- search and practice. Also, for each such design we would have a solution in each relevant transformation language and tool. Unfortunately, this is completely unrealistic from a solution construction perspective as well as from a solution classification and ranking perspective. In this paper, we have adopted a three-step approach: first, so- lution experts build a solution using their own design, language and tool preferences. Then, all solution experts perform a peer review and the results are analysed sta- tistically. Finally, experts critically investigate the sta- tistical results and perform a detailed investigation for suspicious samples. The strength of this approach is that research effort is focussed on solutions that are consid- ered ideal by expert solution builders. In future work, one may investigate the effectiveness of organising exper- iments in a more controlled manner. A challenge could be that solution builders feel overly constrained and there- fore refuse to cooperate in the experiment but this re- quires further investigation.
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An Approach Based On Model Driven Engineering For Big Data Visualization In Different Visual Modes

An Approach Based On Model Driven Engineering For Big Data Visualization In Different Visual Modes

In order to propose tools that claim the MDA architecture published in 2002 by the OMG, the INRIA laboratory in Nantes has developed the ATL model transformation language [32] (ATLAS Transformation Language) that we use in the rest of this thesis. An ATL program is composed of rules that define how elements of the source model are identified and traversed to create the elements of the target model (model-to-model transformation (called Module)). Beyond the transformations of classical models, ATL defines an additional model of request which makes it possible to specify queries on the models (transformations of type model towards text (called Query)). One of the particularities of the ATL language is its hybrid character (declarative and imperative). The declarative gives the possibility to directly correspond an element of the source metamodel of the transformation with an element of the target metamodel of the transformation. Indeed, the example of a completely declarative transformation in ATL is presented below. We describe a rule named Sample, for each InputM element of type InputElement that it identifies in the source model, creates an OutputM element of type OutputElement in a target model, and initializes the value of the attributeA attribute of OutputM with the value of the attributeB attribute of InputM.
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Translation Memory Systems Have a Long Way to Go

Translation Memory Systems Have a Long Way to Go

The TM memory systems changed the work of translators and now the translators not benefiting from these tools are a tiny minority. These tools operate on fuzzy (surface) matching mostly and cannot benefit from already translated texts which are synonymous to (or paraphrased versions of) the text to be translated. The match score is mostly based on character-string similarity, calculated through Levenshtein distance. The TM tools have difficulties with detecting similarities even in sentences which represent a minor revision of sentences already available in the translation memory. This shortcoming of the current TM systems was the subject of the present study and was empirically proven in the experiments we conducted. To this end, we compiled a small translation memory (English-Spanish) and applied several lexical and syntactic transformation rules to the source sentences with both English and Spanish being the source language.
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A Study on Developer Perception of Transformation Languages for Refactoring

A Study on Developer Perception of Transformation Languages for Refactoring

Reducing the manual maintenance effort while refactoring has been the focus of a recent study [2]. We examine this work more closely as a documented example of the advantages of using an automated approach in an industry setting. In that paper, the authors explored several maintenance problems faced by ABB Inc. and designed refactorings in a transformation language (XSLT) to resolve a set of problems. One of the problems they explored occurs due to updates to the C++ standard. Several years ago, a change to the C++ standard dictated that C++’s operator new should throw an exception instead of its previous behavior; returning 0 or null. This caused the need for adaptive maintenance; maintenance whose goal is to update the code base due to a change in the environment. Initially, ABB solved this problem manually. They assigned developers to the task of changing all calls to operator new such that they were wrapped in a try-catch block instead of checking their return value for null.
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Graph Based Semantics of the  NET Intermediate Language

Graph Based Semantics of the NET Intermediate Language

The second example is based on a program of which the building blocks (i. e. the classes) are structured using inheritance. When simulating that program we have to deal with object creation, method resolving, parameter passing, etcetera. For this example a simple C# program is written, compiled and disassembled to IL, which on its turn is translated to an ASG by our translator. This ASG is used as starting point for our simulation. The results of this simulation will be discussed. Simulating a program yields a Labelled Transition System (LTS), which was already briefly introduced in Section 3.2. A LTS is a graph containing nodes and edges. Each edge stands for the application of a production rule and each node represents a graph. Such a graph can be seen as a state – or snapshot – of the system at run-time.
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The Helsinki Neural Machine Translation System

The Helsinki Neural Machine Translation System

handling of morphologically rich languages, such as Finnish. We combine two such approaches: the hybrid character/word model of Luong and Man- ning (2016), which is used for the source language encoder, and the byte-pair encoding (BPE) tech- nique of Sennrich et al. (2016c), which is used for the target language decoder and has been suc- cessfully used for Finnish previously (S´anchez- Cartagena and Toral, 2016). As BPE can be added as a simple pre- and post-processing step, it does not affect the structure of the translation model. This means that our system can be used with char- acter, word and BPE level generation on the tar- get side. The structure of the network, thus, con- sists of three Long Short-Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) layers:
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English language assessment and evaluation practices in the 4th grade classes at main stream schools

English language assessment and evaluation practices in the 4th grade classes at main stream schools

YLLs’ performance along with teacher observation and assessment tasks. In agreement with these methods, exam papers, classical tools, and task materials are used as language assessment tools. Although MoNE (2013, 2017) gives importance to self-assessment, peer-assessment and portfolio assessment, these methods seem to be less used in the 4 th grade classrooms. With respect to language assessment results, they are primarily reported to the young learners in written or oral ways, and YLLs get feedback on their language learning at the end of language assessment. In view of existing language assessment program (MoNE, 2013), objectivity and content validity are considered as the strengths of language assessment at the 4 th grade whereas the allotted time may not be adequate for administering assessment practices. Additionally, it is required to ensure reliability in the current assessment program by promoting objectivity and practicality. Broadly speaking, the reflection of theoretical considerations on classroom practices has been examined in this study. The basic findings show that there is a nexus between theory and practice to some degree, but it should be extended through alternative and language- use oriented assessment methods, techniques and tools. Not only does this argument summarize teacher- assessment practices in young learner classrooms, but also provides decision-makers with feedback on the strengths and weaknesses of the language assessment program (MoNE, 2013). As a further research study, English language assessment practices at other levels of young learner education may be described and examined. Also, the description of English language assessment practices may be enriched through the involvement of other stakeholders, i.e. administrators, policy makers, and teacher educators.
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Delexicalized transfer parsing for low resource languages using transformed and combined treebanks

Delexicalized transfer parsing for low resource languages using transformed and combined treebanks

The systems were ranked based on macro- averaged LAS. The final evaluation of the parser is on blind test data sets (Nivre et al., 2017a) through TIRA platform set up by Potthast et al. (2014). We submitted 9 systems (softwarek, where k ∈ {2, · · · , 10}). The systems differ in the models trained for the surprise languages. The models corresponding to the known language treebanks and the new parallel treebanks were same in all the systems. Since the test set was blind, the first four systems (software 2 to 5) consisted of a com- bination of models for the surprise languages that were expected to perform best based on the per- formance on the sample treebanks. The remaining 5 consisted of models corresponding to combina- tions of top-k (k= 1, 5, 10, 15, 20) models for each of the surprise languages. Table 3.1.3 lists the tree- banks combined to train the models for our pri- mary system. We summarize the macro-averaged LAS scores for the surprise languages for the 8 models in Table 3.3. The highest scoring system for the surprise languages (software2) consists of top-2 model for Buryat, top-10 model for Kur- manji (Kurdish) and top-6 models for North S´ami and Upper Sorbian. The results using the primary system is summarized in Table 3.1 and the macro- average over all submitted softwares are listed in Table 3.3.
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Reordering rules for English Hindi SMT

Reordering rules for English Hindi SMT

For the evaluation purpose we have trained and evaluated three different phrase based SMT systems using MOSES toolkit (Koehn et al. 2007) and GIZA++(Och and Ney, 2003). The first system was non-reordered baseline (Brown et al., 1990; Marcu and Wong, 2002; Koehn et al., 2003), second using limited reordering de- scribed in Ramanathan et al. (2008) and third using improved reordering technique proposed in the paper. Evaluation has been carried out for end to end English-Hindi translation outputs us- ing BLEU score (Papineni et al., 2001), NIST score (Doddington, 2002), multi-reference posi- tion-independent word error rate (Tillmann et al., 1997), multi-reference word error rate (Nießen et al., 2000). We have observed improvement in each of these evaluation metrics used. Next sec- tion discusses related work. Section 3 describes our reordering approach followed by experi- ments and results in section 4 and conclusion in section 5.
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The Accuracy of GIS Tools for Transforming Assumed Total Station Surveys to Real World Coordinates

The Accuracy of GIS Tools for Transforming Assumed Total Station Surveys to Real World Coordinates

There are numerous transformation methods for trans- forming between coordinate systems ranging from sim- ple to sophisticated. The choice of appropriate method depends on the specifics of the application and is gener- ally one that should be made by someone with proper training in surveying and geomatics as well as a solid understanding of the source data and how it was col- lected [13]. In this paper, the transformation tools built in ArcGIS (e.g. spatial adjustment tool and georeferencing tool), and add-in tools such as CHaMP tool were used to transform unprojected total station precise observations into projected real world coordinates. All of these tools use affine transformation. An affine transformation is any transformation that preserves collinearity (i.e., all points lying on a line initially still lie on a line after transforma- tion) and ratios of distances. In general, an affine trans- formation is a composition of rotations, translations, di- lations (scales), and shears (skews) [14]. The transforma- tion functions are based on the comparison of the coor- dinates of source and destination points, also called con- trol points.
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Tools for Transformation Series: Extended Recovery Support

Tools for Transformation Series: Extended Recovery Support

emerged in 1994, following a series of focus groups conducted by the NET division director. These focus groups were composed of people receiving services at NET and they suggested several profound service delivery improvements. The major change was the inclusion of people in recovery in the provision and direction of services at NET. These focus groups had a significant impact on the entire NET system and catalyzed the transformation to a Recovery Management Model.

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Transformation of Natural Language Into Logical Formulas

Transformation of Natural Language Into Logical Formulas

TRANSFORMATION OF NATURAL LANGUAGE INTO LOGICAL FORMULAS COTING 82, J ttorec/ff, (eeL) North Holland Publishing Comply ? Aeuclerala, 1982 TRANSFORMATION OF NATURAL LANGUAGE INTO LOGICAL FORMULAS L e o[.]

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Volume 10: Graph Transformation and Visual Modeling Techniques 2008

Volume 10: Graph Transformation and Visual Modeling Techniques 2008

Previous work in [KTWW06, Klo03] presents a strategy to verify systems against LSC specifi- cations by transforming the LSC to a positive automaton. We use the term positive automaton to denote automaton that witness chart completions. With the positive automaton, a system is veri- fied against the LSC in three stages: reachability analysis for detecting safety violations, ACTL verification for detecting liveness errors, and finally, if the first two steps fail to provide a signif- icant result, full LTL verification is required to completely verify the system. The authors argue that the verification algorithms are applied in increasing order of cost and for certain sub-classes of LSCs not all algorithms need to be applied, which can eventually save on the total verifica- tion cost. Although the approach presented in [KTWW06] is sound, it has several drawbacks. For any arbitrary LSC, the approach at a minimum has to apply reachability analysis as well as ACTL model checking for verifying the safety and liveness properties of the system against the LSC. In the worst case, LTL verification is required to completely verify the system, which was shown to be impractical for LSC verification [KM07]. Another drawback of the verification ap- proach is the specialized algorithms and tools that have to be created to perform the verification, which limit the general applicability and acceptance of the approach. The approach presented in this paper only requires one verification algorithm of the same cost as reachability analysis to completely verify a system against any arbitrary LSC.
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Technology as a tool towards educational reform Implementing Communicative Language Teaching in Georgia

Technology as a tool towards educational reform Implementing Communicative Language Teaching in Georgia

In the sovereign state of Georgia, at the crossroads of Asia and Europe, this issue of technical developments and funding is very topical. After the col- lapse of the Soviet Union in 1991, Georgia looked irreversibly towards the West, and this paved the way for a newly found self-assurance. In the move towards modernization, the educational system received a high priority. The European and American teaching approaches have since been impor- tant models that Georgian education policy makers have been looking up to and trying to emulate. Teaching foreign languages has become one of the top priorities; knowing western languages is considered an invaluable tool that will help open up the doors and help Georgian youth share in the experiences of the Western world. As Communicative Language Teaching is the unofficial western way of teaching, Georgian schools have been try- ing to adopt this approach and, to further modernize the language teaching system, support it with the digital tools available nowadays.
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Language independent model transformation verification

Language independent model transformation verification

Abstract. One hinderance to model transformation verification is the large number of different MT languages which exist, resulting in a large number of different language-specific analysis tools. As an alternative, we define a single analysis process which can, in principle, analyse speci- fications in several different transformation languages, by making use of a common intermediate representation to express the semantics of trans- formations in any of these languages. Some analyses can be performed directly on the intermediate representation, and further semantic models in specific verification formalisms can be derived from it. We illustrate the approach by applying it to ATL.
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Current Approaches To Data Cleaning In Db&Dw System

Current Approaches To Data Cleaning In Db&Dw System

This section classifies the major data quality problems to be solved by data cleaning and data transformation. As we will see, these problems are closely related and should thus be treated in a uniform way. Data transformations are needed to support any changes in the structure, representation or content of data. These transformations become necessary in many situations, e.g., to deal with schema evolution, migrating a legacy system to a new information system, or when multiple data sources are to be integrated. As shown in Fig. we roughly distinguish between single-source and multi- source problems and between schema- and instance-related problems. Schema-level problems of course are also reflected in the instances, they can be addressed at the schema level by an improved schema design (schema evolution), schema translation and schema integration. Instance-level problems, on the other hand, refer to errors and
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Tools for supporting language learning for Sakha

Tools for supporting language learning for Sakha

This paper presents an overview of linguis- tic resources available for the Sakha lan- guage, and presents new tools for support- ing language learning for Sakha. The essen- tial resources include a morphological an- alyzer, digital dictionaries, and corpora of Sakha texts. We extended an earlier version of the morphological analyzer/transducer, built on the Apertium finite-state platform. The analyzer currently has an adequate level of coverage, between 86% and 89% on two Sakha corpora. Based on these resources, we implement a language-learning environ- ment for Sakha in the Revita computer- assisted language learning (CALL) plat- form. Revita is a freely available online lan- guage learning platform for learners beyond the beginner level. We describe the tools for Sakha currently integrated into the Revita platform. To our knowledge, at present this is the first large-scale project undertaken to support intermediate-advanced learners of a minority Siberian language.
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LaTaT: Language and Text Analysis Tools

LaTaT: Language and Text Analysis Tools

In natural language processing, syntactic and semantic knowledge are deeply intertwined with each other, both in their acquisition and usage. The goal of our research is to build a syntactic and semantic knowledge base through an iterative process that involves both language processing and language acquisition. We start the process by parsing a large corpus with a manually constructed parser that has only syntactic knowledge. We then extract lexical semantic and statistical knowledge from the parsed corpus, such as similar words and phrases, collocations and idiomatic expressions, and selectional preferences. In the second cycle, the text corpus is parsed again with the assistance of the newly acquired semantic and statistical knowledge, which allows the parser to better resolve systematic syntactic ambiguities, removing unlikely parts of speech. Our hypothesis is that this will result in higher quality parse trees, which in turn allows extraction of higher quality semantic and statistical knowledge in the second and later cycles.
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Paraphrasing tools, language translation tools and plagiarism: an exploratory study

Paraphrasing tools, language translation tools and plagiarism: an exploratory study

When determining whether there is a potential breach in academic integrity, it is import- ant to distinguish between extremely poor English skills, the use of a LOTE-to-English translation device, and the generation of text through a paraphrasing tool. Carter and Inkpen (2012, p.49) note “Machine translated text often seems to be intuitively identifiable by proficient speakers of a language”. If a student has used paraphrasing tools to alter a text to evade detection of plagiarism, then that act of evasion suggests that plagiarism has occurred. Word matching software such as Turnitin® (n.d.) has proven valuable in identify- ing replication of text from other sources. However, the very purpose of paraphrasing tools is to deceive software developed to detect plagiarism, and it is apparent that to date this strategy has been successful (Lancaster and Clarke 2009; Rogerson and McCarthy 2017; Shahid et al. 2017). Consequently, the burden of detection remains with the human reader who has to become increasing adept at spotting stylistic variations and any other flags relat- ing to mechanisms that have been used to avoid detection (Gillam et al. 2010).
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