Abstract: A device and a method for nondestructively detecting a transient characteristic of a conductive screw of a turbo-generator rotor are provided. The device includes a personal computer (PC), an extremely-steep pulse generator, an ultra-high-frequency double-isolation transformer, and a pulse emitting and coupling module, which are connected in sequence. The pulse emitting and coupling module is connected to a load. A synchronous pulse receiving non-inductive divider circuit synchronously receives a characteristic waveform from the load, and the synchronous pulse receiving non-inductive divider circuit is connected to an ultra-high-speed analog/digital (A/D) module through a nonlinear saturation amplifying circuit that amplifies a signal. The PC receives a signal from the ultra-high-speed A/D module. The load includes a positive or negative excitation lead loop that is in a 180° symmetrical and instantaneous short-circuit state and a rotor shaft.

Abstract: Implementations herein relate to methods and devices for generating candidate translations and for quantizing text as well as words. A method may include generating, by a computing device, pending candidate translations of text to be translated based on predetermined translation rules. The computing device may generate translation probabilities from the text to be translated to the pending candidate translations based on features having impacts on translation probabilities of the pending candidate translations and a predetermined translation probability prediction model. The computing device may then select a predetermined number of pending candidate translations that have the translation probabilities higher than other pending candidate translations in the pending candidate translations to be the candidate translations of the text to be translated.

Abstract: A statistics-based machine translation method is disclosed. The method generates probabilities of translation from a sentence to be translated to candidate translated texts based on features of the candidate translated texts that affect the probabilities of translation and a pre-generated translation probability prediction model. The features that affect probabilities of translation include at least degrees of semantic similarity between the sentence to be translated and the candidate translated texts. A preset number of candidate translated texts with highly ranked probabilities of translation are selected to serve as translated texts of the sentence to be translated. The method is able to go deep into a semantic level of a natural language when a machine translation model is constructed to avoid a semantic deviation of a translated text from an original text, thereby achieving the effect of improving the quality of translation.

Abstract: A statistics-based machine translation method is disclosed. The method generates probabilities of translation from a sentence to be translated to candidate translated texts based on features of the candidate translated texts that affect the probabilities of translation and a pre-generated translation probability prediction model. The features that affect probabilities of translation include at least degrees of semantic similarity between the sentence to be translated and the candidate translated texts. A preset number of candidate translated texts with highly ranked probabilities of translation are selected to serve as translated texts of the sentence to be translated. The method is able to go deep into a semantic level of a natural language when a machine translation model is constructed to avoid a semantic deviation of a translated text from an original text, thereby achieving the effect of improving the quality of translation.

Abstract: Implementations herein relate to methods and devices for generating candidate translations and for quantizing text as well as words. A method may include generating, by a computing device, pending candidate translations of text to be translated based on predetermined translation rules. The computing device may generate translation probabilities from the text to be translated to the pending candidate translations based on features having impacts on translation probabilities of the pending candidate translations and a predetermined translation probability prediction model. The computing device may then select a predetermined number of pending candidate translations that have the translation probabilities higher than other pending candidate translations in the pending candidate translations to be the candidate translations of the text to be translated.

Abstract: A statistics-based machine translation method is disclosed. The method generates probabilities of translation from a sentence to be translated to candidate translated texts based on features of the candidate translated texts that affect the probabilities of translation and a pre-generated translation probability prediction model. The features that affect probabilities of translation include at least degrees of semantic similarity between the sentence to be translated and the candidate translated texts. A preset number of candidate translated texts with highly ranked probabilities of translation are selected to serve as translated texts of the sentence to be translated. The method is able to go deep into a semantic level of a natural language when a machine translation model is constructed to avoid a semantic deviation of a translated text from an original text, thereby achieving the effect of improving the quality of translation.

Abstract: Implementations herein relate to methods and devices for generating candidate translations and for quantizing text as well as words. A method may include generating, by a computing device, pending candidate translations of text to be translated based on predetermined translation rules. The computing device may generate translation probabilities from the text to be translated to the pending candidate translations based on features having impacts on translation probabilities of the pending candidate translations and a predetermined translation probability prediction model. The computing device may then select a predetermined number of pending candidate translations that have the translation probabilities higher than other pending candidate translations in the pending candidate translations to be the candidate translations of the text to be translated.

Abstract: A translation method is disclosed herein. The method includes determining a target object to be translated, the target object including a plurality of elements; dividing the target object to be translated according to a language correspondence relationship to obtain at least one element set; determining a weight value of a second object corresponding to each first object in each element set according to the language correspondence relationship; determining a comparison value associated with each element set according to the determined weight value and selecting an element set with the maximum comparison value; determining a second object with the maximum weight value corresponding to each first object in the selected element set according to the correspondence relationship, combining all the determined second objects to form a translation content of the target object.

Abstract: A machine translation method includes determining source language text to be translated and obtaining a translation rule table, which has been trained in advance, that includes multiple translation rules associated with the target language text and the source language text in multiple languages; determining candidate results of the target language text; and determine the target language text to be output based on the candidate results. During the translation, a specific language of the source language text need not to be specified by a user. The implementations improve accuracy of the translation, and avoid errors introduced from the process of language identification during recognizing unknown languages. The implementations avoid developing a translation engine for an individual source language of text to be translated for a certain target language, and therefore save development costs and computing resources.

Abstract: A machine translation training system is provided. The system includes a task distribution server, a plurality of mapping servers, and a plurality of reduction servers. During operation, the task distribution server is configured to distribute a first translation training task and a training corpus to the mapping servers and distribute a second translation training task and first translation training results received from the mapping servers to the reduction servers. A respective mapping server is configured to receive a portion of the training corpus and perform the first translation training task on the received portion of the training corpus. A respective reduction server is configured to receive a subset of the first translation training results that correspond to a same language element, perform the second translation training task on the received subset of the first translation training results, and output second translation training results.

Abstract: Embodiments of the present application provide a method and system for statistics-based machine translation. During operation, the system may obtain at least one text to be translated and localized information. The system may decode the text to be translated. The system may then generate a plurality of candidate translations for the text to be translated. For each candidate translation of the plurality of candidate translations, the system may obtain linguistic translation features according to the text to be translated and the candidate translation. The system may extract localized translation features according to the localized information. The system may then apply a translation quality prediction model to calculate translation quality scores for the plurality of candidate translations according to the linguistic translation features and the localized translation features.

Abstract: A translation method is disclosed herein. The method includes determining a target object to be translated, the target object including a plurality of elements; dividing the target object to be translated according to a language correspondence relationship to obtain at least one element set; determining a weight value of a second object corresponding to each first object in each element set according to the language correspondence relationship; determining a comparison value associated with each element set according to the determined weight value and selecting an element set with the maximum comparison value; determining a second object with the maximum weight value corresponding to each first object in the selected element set according to the correspondence relationship, combining all the determined second objects to form a translation content of the target object.

Abstract: A statistics-based machine translation method is disclosed. The method generates probabilities of translation from a sentence to be translated to candidate translated texts based on features of the candidate translated texts that affect the probabilities of translation and a pre-generated translation probability prediction model. The features that affect probabilities of translation include at least degrees of semantic similarity between the sentence to be translated and the candidate translated texts. A preset number of candidate translated texts with highly ranked probabilities of translation are selected to serve as translated texts of the sentence to be translated. The method is able to go deep into a semantic level of a natural language when a machine translation model is constructed to avoid a semantic deviation of a translated text from an original text, thereby achieving the effect of improving the quality of translation.

Abstract: Implementations herein relate to methods and devices for generating candidate translations and for quantizing text as well as words. A method may include generating, by a computing device, pending candidate translations of text to be translated based on predetermined translation rules. The computing device may generate translation probabilities from the text to be translated to the pending candidate translations based on features having impacts on translation probabilities of the pending candidate translations and a predetermined translation probability prediction model. The computing device may then select a predetermined number of pending candidate translations that have the translation probabilities higher than other pending candidate translations in the pending candidate translations to be the candidate translations of the text to be translated.

Abstract: A machine translation method includes determining source language text to be translated and obtaining a translation rule table, which has been trained in advance, that includes multiple translation rules associated with the target language text and the source language text in multiple languages; determining candidate results of the target language text; and determine the target language text to be output based on the candidate results. During the translation, a specific language of the source language text need not to be specified by a user. The implementations improve accuracy of the translation, and avoid errors introduced from the process of language identification during recognizing unknown languages. The implementations avoid developing a translation engine for an individual source language of text to be translated for a certain target language, and therefore save development costs and computing resources.

Abstract: A machine translation training system is provided. The system includes a task distribution server, a plurality of mapping servers, and a plurality of reduction servers. During operation, the task distribution server is configured to distribute a first translation training task and a training corpus to the mapping servers and distribute a second translation training task and first translation training results received from the mapping servers to the reduction servers. A respective mapping server is configured to receive a portion of the training corpus and perform the first translation training task on the received portion of the training corpus. A respective reduction server is configured to receive a subset of the first translation training results that correspond to a same language element, perform the second translation training task on the received subset of the first translation training results, and output second translation training results.