Abstract: A text translation method includes determining at least one first probability based on a first text feature of a first text in a first language to indicate probabilities that the first text is translated into various candidate texts in a second language; obtaining at least one target data-pair matching the first text feature, a target data-pair comprising one second text feature and one standard translation text of second text, the second text being in the first language, and the standard translation text being in the second language; determining confidences and matching degrees of the at least one target data-pair; determining at least one second probability to indicate probabilities that the first text is translated into various standard translation texts in the at least one target data-pair; and determining a translation text corresponding to the first text.

Abstract: A computer device acquires a semantic association graph associated with n source statements belonging to different modals. The semantic association graph includes n semantic nodes of the different modals, a first connecting edge used for connecting the semantic nodes of a same modal and a second connecting edge used for connecting the semantic nodes of different modals. The computer device extracts a plurality of first word vectors from the semantic association graph. The device encodes the plurality of first word vectors to obtain n encoded feature vectors. The device also decodes the n encoded feature vectors to obtain a translated target statement.

Abstract: A computer device acquires a semantic association graph associated with n source statements belonging to different modals. The semantic association graph includes n semantic nodes of the different modals, a first connecting edge used for connecting the semantic nodes of a same modal and a second connecting edge used for connecting the semantic nodes of different modals. The computer device extracts a plurality of first word vectors from the semantic association graph. The device encodes the plurality of first word vectors to obtain n encoded feature vectors. The device also decodes the n encoded feature vectors to obtain a translated target statement.

Abstract: Embodiments of the present invention provide a method for controlling character animation, in which the character animation includes at least two bones and skins corresponding to the bones, the method includes: (a) dividing the character animation into at least two parts, and setting an identification number for each part; (b) establishing a mapping table comprising a corresponding relationship between the identification number and skin data of each part; (c) picking skin data of an operation focus location in the character animation; (d) querying the mapping table according to the skin data, obtaining a corresponding identification number, and controlling the part in the character animation corresponding to the identification number. Embodiments of the present invention also provide a system for controlling character animation. Different parts of the character animation may be picked respectively by dividing the character animation into multiple parts.