Abstract: A translation method is provided, including: encoding to-be-processed text information to obtain a source vector representation sequence, the to-be-processed text information belonging to a first language; obtaining a source context vector corresponding to a first instance according to the source vector representation sequence, the source context vector indicating to-be-processed source content in the to-be-processed text information at the first instance; determining a translation vector according to the source vector representation sequence and the source context vector; and decoding the translation vector and the source context vector, to obtain target information of the first instance, the target information belonging to a second language.

Abstract: The method includes: obtaining a to-be-translated source document, where the source document includes at least one character of a source language; converting the source document into a plurality of target documents respectively by using a plurality of machine translation apparatuses, where one machine translation apparatus is configured to translate the source document into one target document, the target document includes at least one character of a target language, and the source language is different from the target language; determining a feature value of each preset feature of each target document; determining a recommendation rating of each target document based on the feature value of each preset feature of each target document; and outputting a target document with a highest recommendation rating based on the recommendation rating of each target document.

Abstract: A translation model based training method is provided for a computer device. The method includes inputting a source sentence to a translation model, to obtain a target sentence outputted by the translation model; determining a fidelity of the target sentence to the source sentence; using the target sentence and a reference sentence as input of a discriminator model, using the fidelity as output of the discriminator model, and training the discriminator model on a performance of calculating a similarity between the target sentence and the reference sentence; outputting the similarity by using the discriminator model; and using the source sentence as input of the translation model, using the target sentence as output of the translation model, and using the similarity as a weight coefficient, and training the translation model on a performance of outputting the corresponding target sentence according to the input source sentence.

Abstract: A content explanation method and apparatus applied to content explanation includes identifying, by a content explanation apparatus, an emotion of the user, when identifying a negative emotion showing that the user is confused about delivered multimedia information, obtaining, by the content explanation apparatus, a target representation manner of target content in a target intelligence type, where the target content is content about which the user is confused in the multimedia information delivered to the user by an information delivery apparatus associated with the content explanation apparatus, and presenting, by the content explanation apparatus, the target content to the user in the target representation manner.

Abstract: A machine translation method and an apparatus are provided. The method includes: obtaining, when translating a word fragment from a first language into a second language, a source representation vector of the word fragment. The source representation vector of the word fragment represents the word fragment in the first language. The method also includes obtaining a historical translation vector of the word fragment by querying historical translation information according to the source representation vector of the word fragment. The historical translation vector of the word fragment represents a historical translation situation corresponding to the word fragment. The method further includes translating the word fragment according to the historical translation vector of the word fragment.

Abstract: Embodiments of the present invention provide a translation method and apparatus, and relate to the field of machine translation. The method includes: obtaining a to-be-translated sentence, where the to-be-translated sentence is a sentence expressed in a first language; determining a first named entity set in the to-be-translated sentence, and an entity type of each first named entity in the first named entity set; determining, based on the first named entity set and the entity type of each first named entity, a second named entity set expressed in a second language; determining a source semantic template of the to-be-translated sentence, and obtaining a target semantic template corresponding to the source semantic template from a semantic template correspondence; and determining a target translation sentence based on the second named entity set and the target semantic template.

Abstract: A sequence conversion method includes receiving a source sequence, converting the source sequence into a source vector representation sequence, obtaining at least two candidate target sequences and a translation probability value of each of the at least two candidate target sequences according to the source vector representation sequence, adjusting the translation probability value of each candidate target sequence, selecting an output target sequence from the at least two candidate target sequences according to an adjusted translation probability value of each candidate target sequence, and outputting the output target sequence. Hence, loyalty of a target sequence to a source sequence can be improved during sequence conversion.

Abstract: The present application relates to natural language processing and discloses a sequence conversion method. The method includes: obtaining a source sequence from an input signal; converting the source sequence into one or more source context vectors; obtaining a target context vector corresponding to each source context vector; combining the target context vectors to obtain the target sequence; and outputting the target sequence. A weight vector is applied on a source context vector and a reference context vector, to obtain a target context vector. The source sequence and the target sequence are representations of natural language contents. The claimed process improves faithfulness of converting the source sequence to the target sequence.

Abstract: The present invention relates to the use of a plurality of dependency trees in tree-based statistical machine translation and proposes a dependency forest to effectively process the plurality of dependency trees. The present invention can improve a translation capability by generating a translation rule and a dependency language model by using the dependency forest and applying the generated translation rule and dependency language model when a source language text is converted to a target language text.

Abstract: Disclosed embodiments include a neural network-based translation method, including: splitting the unknown word in an initial translation into one or more characters, and inputting, into a first multi-layer neural network, a character sequence constituted by the one or more characters ; obtaining a character vector of each character in the character sequence by using the first multi-layer neural network, and inputting all character vectors in the character sequence into a second multi-layer neural network; encoding all the character vectors by using the second multi-layer neural network and a preset common word database, to obtain a semantic vector; and inputting the semantic vector into a third multi-layer neural network, decoding the semantic vector by using the third multi-layer neural network, and determining a final translation of the to-be-translated sentence based on the initial translation of the to-be-translated sentence.

Abstract: The present invention relates to the use of a plurality of dependency trees in tree-based statistical machine translation and proposes a dependency forest to effectively process the plurality of dependency trees. The present invention can improve a translation capability by generating a translation rule and a dependency language model by using the dependency forest and applying the generated translation rule and dependency language model when a source language text is converted to a target language text.

Abstract: A syntax analysis method and apparatus are disclosed. The method includes: obtaining a source language sentence that is a translation of a target language sentence (S110); determining instances of state transition for the target language sentence according to the source language sentence and a correspondence between words of the target language sentence and words of the source language sentence (S120); and generating a syntax tree of the target language sentence according to the instances of state transition for the target language sentence (S130). The syntax analysis method and apparatus can improve efficiency of syntax analysis.

Abstract: The present invention relates to the use of a plurality of dependency trees in tree-based statistical machine translation and proposes a dependency forest to effectively process the plurality of dependency trees. The present invention can improve a translation capability by generating a translation rule and a dependency language model by using the dependency forest and applying the generated translation rule and dependency language model when a source language text is converted to a target language text.