Abstract: The present invention relates to a torque transmission device (2) comprising a planetary gear train (22), which has a rotatable component (28), a stationary housing (18), and a clutch device (32) for selective coupling of the component (28) with the stationary housing (18). The clutch device (32) has a friction-locking first clutch (34) and a positive-locking second clutch (36), wherein the component (28) may be coupled to the stationary housing (18) via the first clutch (34) before a coupling by means of the second clutch (36).

Abstract: Specialized language processing engines can use author-specific or reader-specific language models to improve language processing results by selecting phrases most likely to be used by an author or by tailoring output to language with which the reader is familiar. Language models that are author-specific can be generated by identifying characteristics of an author or author type such as age, gender, and location. An author-specific language model can be built using, as training data, language items written by users with the identified characteristics. Language models that are reader-specific can be generated using, as training data, language items written by or viewed by that reader. When implementing a specialized machine translation engine, multiple possible translations can be generated. An author-specific language model or a reader-specific language model can provide scores for possible translations, which can be used to select the best translation.

Abstract: Technology is disclosed for mining training data to create machine translation engines. Training data can be mined as translation pairs from single content items that contain multiple languages; multiple content items in different languages that are related to the same or similar target; or multiple content items that are generated by the same author in different languages. Locating content items can include identifying potential sources of translation pairs that fall into these categories and applying filtering techniques to quickly gather those that are good candidates for being actual translation pairs. When actual translation pairs are located, they can be used to retrain a machine translation engine as in-domain for social media content items.

Abstract: Technology is disclosed that improves language coverage by selecting sentences to be used as training data for a language processing engine. The technology accomplishes the selection of a number of sentences by obtaining a group of sentences, computing a score for each sentence, sorting the sentences based on their scores, and selecting a number of sentences with the highest scores. The scores can be computed by dividing a sum of frequency values of unseen words (or n-grams) in the sentence by a length of the sentence. The frequency values can be based on posts in one or more particular domains, such as the public domain, the private domain, or other specialized domains.

Abstract: Technology is disclosed for building correction models that correct natural language snippets. Correction models can include rules comprising pairs of word sequences identified from viable correction snippet pairs, where a first sequence of words in the pair should be replaced with a second sequence of words in the pair. Viable correction snippet pairs can be identified from among pairs of language snippets, such as a post to a social media website and a subsequent update to that post. Viable corrections can be the snippet pairs that both have no more unaligned words than a word alignment threshold and have no aligned word pair with a character edit difference above an edit distance threshold. In some implementations, word alignments can be found by aligning all the characters between a pair of language snippets, and identifying aligned words as those that have at least one aligned letter in common.

Abstract: Technology is disclosed for mining training data to create machine translation engines. Training data can be mined as translation pairs from single content items that contain multiple languages; multiple content items in different languages that are related to the same or similar target; or multiple content items that are generated by the same author in different languages. Locating content items can include identifying potential sources of translation pairs that fall into these categories and applying filtering techniques to quickly gather those that are good candidates for being actual translation pairs. When actual translation pairs are located, they can be used to retrain a machine translation engine as in-domain for social media content items.

Abstract: Exemplary embodiments provide techniques for evaluating when words or phrases of a translation were generated with a low degree of confidence, and conveying this information when the translation is presented. For example, if a source language word is encountered in source material for translation, but the source language word was only encountered a few times (or not at all) in the training data used to train the translation system, then the resulting translation may be flagged as being of low confidence. Other situations, such as the generation of two equally-likely translations, or translation system model disagreement, may also indicate a questionable translation. When the translation is displayed, questionable words and phrases may be flagged, and possible alternative translations may be presented. If one of the alternatives is selected, this information may be used to update the translation system's models in order to improve translation quality in the future.

Abstract: Exemplary embodiments relate to detecting, removing, and/or replacing objectionable words and phrases in a machine-generated translation. A classifier identifies translations containing target words or phrases. The classifier may be applied to the output translation to remove target words and phrases from the translation, or to prevent target words and phrases from being automatically presented. Further, the classifier may be applied to a translation model to prevent the target words and phrases from appearing in the output translation. Still further, the classifier may be applied to training data so that the translation model is not trained using the target words of phrases. The classifier may remove target words or phrases only when the target words or phrases appear in the output translation but not the source language input data. The classifier may be provided as a standalone service, or may be employed in the context of a machine translation system.

Abstract: Exemplary embodiments relate to techniques for improving machine translation systems. The machine translation system may apply one or more models for translating material from a source language into a destination language. The models are initially trained using training data. According to exemplary embodiments, supplemental training data is used to train the models, where the supplemental training data uses in-domain material to improve the quality of output translations. In-domain data may include data that relates to the same or similar topics as those expected to be encountered in a translation of material from the source language into the destination language. In-domain data may include material previously translated from the source language into the destination language, material similar to previous translations, and destination language material that has previously been the subject of a request for translation into the source language.

Abstract: Exemplary embodiments relate to detecting, removing, and/or replacing objectionable words and phrases in a machine-generated translation. A classifier identifies translations containing target words or phrases. The classifier may be applied to the output translation to remove target words and phrases from the translation, or to prevent target words and phrases from being automatically presented. Further, the classifier may be applied to a translation model to prevent the target words and phrases from appearing in the output translation. Still further, the classifier may be applied to training data so that the translation model is not trained using the target words of phrases. The classifier may remove target words or phrases only when the target words or phrases appear in the output translation but not the source language input data. The classifier may be provided as a standalone service, or may be employed in the context of a machine translation system.

Abstract: An illuminating device for a vehicle includes a carrier module, a flat light guide attached to the carrier module with a plurality of emitting points, and multiple light sources for illuminating the light guide. The flat light guide is concealed with a translucent haptic layer, which is attached to a visible side of the light guide, which, in turn, is covered by a decorative layer. The illuminating device comprises a rope-like light guide, which has an inherent light source and is connected to the decorative layer via a retaining device. This therefore results in a flat arrangement, which can be easily integrated into the interior parts and simultaneously enables illumination over a large surface area. In doing so, additional light effects can be integrated by means of the rope-like light guide. The illuminating device is not visible while switched off, as the illuminating device is covered by the decorative layer.

Abstract: Technology is discussed herein for identifying trending actions within a group of posts matching a query. A group of posts can be selected based on specified actions, action targets, or parameters such as author age, location, gender, when the posts were posted or what keywords they contain. Selected posts can be divided into sentences and a dependency structure can be created for each sentence classifying portions of the sentence as actions or action targets. Statistics can be generated for each sentence or post indicating whether it matches the actions, action targets, or other parameters specified in the query. Based on these statistics, additional information can be gathered to respond to questions posed in the query.

Abstract: Technology is discussed herein for identifying comparatively trending topics between groups of posts. Groups of posts can be selected based on parameters such as author age, location, gender, etc., or based on information about content items such as when they were posted or what keywords they contain. Topics, as one or more groups of words, can each be given a rank score for each group based on the topic's frequency within each group. A difference score for selected topics can be computed based on a difference between the rank score for the selected topic in each of the groups. When the difference score for a selected topic is above a specified threshold, that selected topic can be identified as a comparatively trending topic.

Abstract: A number of variations may include a clutch apparatus having at least one clutch and having an actuating device for actuating the clutch, the actuating device being movable from an opening position, in which the clutch is open, into a closing position, in which the clutch is closed. The actuating device can be locked with form-fit action in the closing position or opening position.

Abstract: The present invention relates to a freewheel (2) comprising an inner ring (30), an outer ring (28), a clamping gap (32) between the inner ring (30) and the outer ring (28), a side wall (40) for lateral limitation of the clamping gap (32) and at least one clamping element (34) in the clamping gap (32) which is biased by means of a spring element (66), wherein the spring element (66) is supported or supportable on one side on the clamping element (34) and on the other side on a support projection (70), which is formed by a supporting tongue (74) bent out of the side wall (40) and projecting into the clamping gap (32). In addition, the present invention relates to a method for producing such a freewheel (2).

Abstract: Specialized language processing engines can use author-specific or reader-specific language models to improve language processing results by selecting phrases most likely to be used by an author or by tailoring output to language with which the reader is familiar. Language models that are author-specific can be generated by identifying characteristics of an author or author type such as age, gender, and location. An author-specific language model can be built using, as training data, language items written by users with the identified characteristics. Language models that are reader-specific can be generated using, as training data, language items written by or viewed by that reader. When implementing a specialized machine translation engine, multiple possible translations can be generated. An author-specific language model or a reader-specific language model can provide scores for possible translations, which can be used to select the best translation.

Abstract: Technology is disclosed that improves language coverage by selecting sentences to be used as training data for a language processing engine. The technology accomplishes the selection of a number of sentences by obtaining a group of sentences, computing a score for each sentence, sorting the sentences based on their scores, and selecting a number of sentences with the highest scores. The scores can be computed by dividing a sum of frequency values of unseen words (or n-grams) in the sentence by a length of the sentence. The frequency values can be based on posts in one or more particular domains, such as the public domain, the private domain, or other specialized domains.

Abstract: The present invention relates to a freewheel (4) comprising a first race (38), a second race (40), a clamping gap (46) designed between the first and second race (38, 40), at least one clamping element (48) in the clamping gap (46), and biasing means (64) for biasing the clamping element (48) into a clamping position within the clamping gap (46), wherein the biasing means (64) have an accordion spring (66), which is supported directly or indirectly on the first race (38) and which comprises corrugation peaks (70) lying radially outward, corrugation troughs (72) lying radially inward, and a support leg (76) for direct or indirect support on the clamping element (48). The biasing means (64) have a spring support element (80) on which at least one of the corrugation peaks (70) is supported or supportable outwardly in radial direction (20) and/or at least one of the corrugation troughs (72) is supported or supportable inwardly in radial direction (22).

Abstract: Technology is disclosed that improves language coverage by selecting sentences to be used as training data for a language processing engine. The technology accomplishes the selection of a number of sentences by obtaining a group of sentences, computing a score for each sentence, sorting the sentences based on their scores, and selecting a number of sentences with the highest scores. The scores can be computed by dividing a sum of frequency values of unseen words (or n-grams) in the sentence by a length of the sentence. The frequency values can be based on posts in one or more particular domains, such as the public domain, the private domain, or other specialized domains.

Abstract: The present invention relates to a freewheel comprising an outer ring, an inner ring, and at least one clamping element which is between the outer ring and the inner ring, which is moveable from a clamping position into a release position along a race on the outer ring in a first circumferential direction relative to the outer ring, wherein the race has a clamping section, and a release section following the clamping section in the first circumferential direction on which the clamping element is supportable. A first tangent through at least one support point on the clamping section defines a first inner angle, which is greater than 180°, with a second tangent through at least one support point on the release section. In addition, the present invention relates to a freewheel arrangement for a motor vehicle comprising such a freewheel.