Sap Se

Michail Vasiltschenko

Ralph Debusmann

Yvonne Miklosch

Anton Snitko

Iuliia Konstantinova

Florian Hoffmann

Benjamin Schork

Jens Scharnbacher

Charles Monte

Christian Lieske

Jochen Geib

Thomas Alcantara Kliche

Yu Zhang

Slim Trabelsi

Annika Berger

Susanne Hempel

Tetyana Chernenko

Elena Lopushenko

Holger Sievert

Günther Fischer

Guenther Fischer

Abdallah El Sahhar

Klaus Heitlinger

Mark Childress

Robert Stuehler

Hanne Smaadahl

Ying Zou

Translation capability for language processing determines an existence of an abbreviation, followed by non-exact matching to map the abbreviation to the original full term. A received string in a source language is provided as input to a translation service. Translation proposals in a different target language are received back. A ruleset (considering factors, e.g., camel case format, the presence of a concluding period, and/or consecutive consonants) is applied to generate abbreviation candidates from the translation proposals. Non-exact matching (referencing e.g., a comparison metric) may then be used to map the abbreviation candidates to text strings of their original full terms. A mapping of the abbreviation to the text string of the original full term is stored in a translation database comprising linguistic data. Embodiments leverage existing resources (e.g., translation service, non-exact matching) to reduce effort and expense of accurately identifying abbreviations and then mapping them to their full original terms.

Detection of abbreviation and mapping to full original term

An engine interposed between an application translator and UI intercepts string templates populated with string variables and output by the translator. The engine determines translation-resistance of the string template. Such determination can be based upon an existing mark inserted by the translator, a number of string variables in the string template, a comment in the string template, user settings, or syntax rules. Frequently, translation-resistance of the string template is not indicated and the engine simply forwards on the string template to the user. Less frequently, the engine determines the string template to be resistant to translation. Then, the engine causes the string template to be processed according to a prefix and a suffix inserted thereto. The processing can comprise forwarding the string template for machine translation, and/or falling back to a simpler string variant. The modified content resulting from the processing is communicated by the engine to the user.

Processing of translation-resistant content in web application

Techniques and solutions are described for translating strings in a human language that are associated with code, such as code that declares user interface elements in association with which the strings can be displayed. The strings can be embedded in code, or can be included in files that are accessed by the code or used in creating executable code. Strings can be extracted from data, such as files, received in a translation request. After translation, such as by a human translator or using automatic translation, the translation results can be provided in response to the translation request. The translation process can be automated, such that changes to files in a code repository can trigger a translation request. Disclosed technologies can facilitate the production of programs that are localized using different human languages.

String extraction and translation service

Disclosed herein are system, method, and computer program product embodiments for crawling feedback sources and generating a product improvement recommendation. In an embodiment, a Product Optimization Crawler and Monitor (POCM) system may crawl feedback comments from different sources such as an Internet source or a customer feedback database. The POCM system may apply artificial intelligence, natural language processing, and constraint modeling techniques to the feedback comments to identify product features as well as a feedback category corresponding to the product feature. The feedback category may include a positive, negative, or neutral feedback category. Using this information, the POCM system may generate a summary of the feedback from different sources and/or generate product improvement recommendation. The product improvement recommendation may include suggesting that a component from a first product be replaced with a similar component from a second product.

Product optimization crawler and monitor

Various examples are directed to systems and methods for identifying textual information regarding a first topic. A computer system may access a plurality of text units and detect that a first text unit of the plurality of text units is in a first language. The computer system may access a first language keyword set for the first topic, where the first language keyword set comprises a first plurality of keywords associated with the first language. The computer system may determine a first relevance score for the first text unit based at least in part on the first language keyword set. If the first relevance score is greater than a relevance score threshold, the computer system translates the first text unit to a base language and determines a text unit classification for the first text unit using a classification model trained with training data in the base language.

Computer systems for classifying multilingual text

An input terminology is received in a source language to determine a target language proposal. In a proposal engine when a configuration profile is available the following steps are performed. A set of target language equivalents are determined by applying a user-defined set of approaches to the input terminology. Statistics is computed corresponding to the set of target language equivalents. A target language proposal from the set of target language equivalents is determined based on the computed statistics. The target language proposal along with the computed statistics is displayed in a graphical user interface.

Terminology proposal engine for determining target language equivalents

Methods, systems, and computer-readable storage media for providing a list of similar texts to a user developing a software program include actions of receiving an original source text (OST) from the user, the OST including a text string, providing an OST with quality correction (OST-QC), the OST-QC including one or more corrections to the OST, determining that a source text repository includes entries associated with one or more source texts that are sufficiently similar, and in response, providing a list of similar texts, the list of similar texts including one or more OST-QCs of respective entries in the source text repository, and transmitting the list of similar texts for display to the user, a similar text of the list of similar texts being automatically inserted into the software program in response to user selection of the similar text from the list of similar texts.

Reducing translation volume and ensuring consistent text strings in software development

A system includes a memory and a semiconductor-based processor coupled to form logic circuits. The logic circuits provide a combined localization and personalization (LPN) web service to a computer application, receive a call from the computer application for recommendations to localize and personalize a computer application for a particular user, prepare a recommendation on which one or more reusable application logic modules to attach to a core logic of the computer application to localize and personalize application for the particular user.

Localized and personalized application logic

Disclosed is an improved system for performing intelligent text reduction on text strings for optimal display in a variety of user interface display elements. The system includes a smart text reduction algorithm that can shorten text strings as necessary in response to resizing the user interface or elements therein. The system also includes a design time user interface for application developers (or other users) to design graphical user interfaces in a collaborative workspace. The design time user interface incorporates the smart text reduction algorithm to enable designers to modify user interface display elements and run the text reduction algorithm on any text strings that no longer fit within the modified display elements. The described embodiments can also translate text strings of user interface display elements and can execute the smart text shortening algorithm on the translated text strings accordingly.

Time user interface with intelligent text reduction

Various embodiments of systems, computer program products, and methods to assess translation quality are described herein. In an aspect, a translated text is received during translation of content from a source language to a target language. The received translated text is detected as an incorrect translation by analyzing a number of quality checks in a translation quality algorithm. An error category corresponding to the incorrect translation is determined based on a root cause of the incorrect translation. Further, a counter is incremented in a corresponding error category. When the counter exceeds a threshold, an action to prevent submission of the translation is triggered based on a combined error score. The combined error score is a combined weighted error score from error categories which have exceeded their respective thresholds.

Assessing translation quality

Various embodiments of systems, computer program products, and methods to provide instant translation of user interfaces of a web application are described herein. In an aspect, a request is received to translate a user interfaces of a web application from a source language to a target language. A translatable user interface element associated with the user interface of the web application is determined. Further, the translatable user interface element is translated from the source language to the target language based on dynamic metadata associated with the translatable user interface element. The translated translatable user interface element in the user interface of the web application is rendered.

Instant translation of user interfaces of a web application

Intelligent text reduction for graphical interface elements

The embodiments may be adapted to accommodate a plurality of translations of a source text string into a limited available display area of a visual element in a graphical interface of a client system. Text strings in a source language may be received as input. A list of one or more languages selected for the text string to be translated into may also be received as input. The system may then be configured to obtain translations of the text string in the selected languages, to provide a set of translation vectors, and to calculate a minimum display area necessary for a visual element to display the longest translation of the translations contained in the translation vector. The display area of the visual element may then be adjusted to encompass the minimum display area so that the longest translation fits within the display area of the visual element.

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Schriesheim, Germany

Michail Vasiltschenko