A method includes: acquiring an input sentence in a first language in a current round of conversation; translating the input sentence in the first language to obtain an input sentence in a second language, according to dialogue contents in the first language and dialogue contents in the second language that have a mutual translation relationship with the dialogue contents in the first language in historical rounds of conversation; invoking a multi-round conversation generation model to parse the input sentence in the second language in the current round of conversation to generate an output sentence in the second language in the current round of conversation; translating the output sentence in the second language in the current round of conversation to obtain at least one candidate result in the first language; and determining an output sentence in the first language from the at least one candidate result in the first language.

The present disclosure provides a method, apparatus, electronic device and readable storage medium for translation and relates to translation technologies. In the embodiments of the present disclosure, the at least one knowledge element is obtained according to associated information of content to be translated, and respective knowledge element in the at least one knowledge element comprise an element of the first language type and an element of the second language type so that the at least one knowledge element can be used to obtain a translation result of the content to be translated. Since the at least one knowledge element obtained in advance is taken as global information of the translation task of this time, it can be ensured that the translation result of the same content to be translated is consistent, thereby improving the quality of the translation result.

Using a natural language processing (NLP) engine executing in conjunction with a machine that is engaged in first natural language interaction, an analytics intent comprising an analysis type to be performed on a dataset is extracted from the first natural language interaction. Within the dataset, a subset of the dataset comprising data having above a threshold relevance measure with respect to the analytics intent is identified. From the subset, a knowledge graph modeling a set of relationships between data in the subset is constructed. Using the analytics intent and the knowledge graph, a conversational template is customized, augmenting the conversational template with a set of entities corresponding to the analytics intent. To obtain a result, the subset is analyzed using the knowledge graph. A second natural language interaction is presented via the machine, the presenting comprising transforming by the NLP engine the result to fit the customized conversational template.

Using a natural language processing (NLP) engine executing in conjunction with a machine that is engaged in first natural language interaction, an analytics intent comprising an analysis type to be performed on a dataset is extracted from the first natural language interaction. Within the dataset, a subset of the dataset comprising data having above a threshold relevance measure with respect to the analytics intent is identified. From the subset, a knowledge graph modeling a set of relationships between data in the subset is constructed. Using the analytics intent and the knowledge graph, a conversational template is customized, augmenting the conversational template with a set of entities corresponding to the analytics intent. To obtain a result, the subset is analyzed using the knowledge graph. A second natural language interaction is presented via the machine, the presenting comprising transforming by the NLP engine the result to fit the customized conversational template.

A system for receiving a corpus of sign language data in which a plurality of known signs each correspond to known meanings, generate a model for identifying new sign language signs using the corpus, and identifying, using the model, a new sign language sign that does not match any of the plurality of known signs.

A system for receiving a corpus of sign language data in which a plurality of known signs each correspond to known meanings, generate a model for identifying new sign language signs using the corpus, and identifying, using the model, a new sign language sign that does not match any of the plurality of known signs.

The present disclosure provide a method and apparatus for translating an application, which relate to the field of information processing technology. The present disclosure enables: detecting time information in the application to be translated; acquiring attribute information of the time information, the attribute information including at least one of target translation languages of the time information, time units contained in the time information, and a number of bits of the data of individual time units of the time information; determining a display format according to the attribute information; and translating the time information based on the display format.

The present disclosure provide a method and apparatus for translating an application, which relate to the field of information processing technology. The present disclosure enables: detecting time information in the application to be translated; acquiring attribute information of the time information, the attribute information including at least one of target translation languages of the time information, time units contained in the time information, and a number of bits of the data of individual time units of the time information; determining a display format according to the attribute information; and translating the time information based on the display format.

A method and apparatus are provided that analyzing sequence-to-sequence data, such as sequence-to-sequence speech data or sequence-to-sequence machine translation data for example, by minimum Bayes risk (MBR) training a sequence-to-sequence model and within introduction of applications of softmax smoothing to an N-best generation of the MBR training of the sequence-to-sequence model.

A method and apparatus are provided that analyzing sequence-to-sequence data, such as sequence-to-sequence speech data or sequence-to-sequence machine translation data for example, by minimum Bayes risk (MBR) training a sequence-to-sequence model and within introduction of applications of softmax smoothing to an N-best generation of the MBR training of the sequence-to-sequence model.