A method, an apparatus, a device and a storage medium for outputting information are provided. The method includes: acquiring a to-be-translated sentence; determining a foreign language sentence matching the to-be-translated sentence from a preset set of sentence pairs, where the set of sentence pairs includes local language sentences and corresponding foreign language sentences; determining a target foreign language sentence of the to-be-translated sentence according to the to-be-translated sentence and the determined foreign language sentence; and outputting the target foreign language sentence.

A computer implemented method for the automated analysis or use of data is implemented by a voice assistant. The method comprises the steps of: (a) storing in a memory a structured, machine-readable representation of data that conforms to a machine-readable language (‘machine representation’); the machine representation including representations of user speech or text input to a human/machine interface; and (b) automatically processing the machine representations to analyse the user speech or text input.

A translation method includes: selecting a source word from a source sentence; generating mapping information including location information of the selected source word mapped to the selected source word in the source sentence; and correcting a target word, which is generated by translating the source sentence, based on location information of a feature value of the target word and the mapping information.

A translation method includes: selecting a source word from a source sentence; generating mapping information including location information of the selected source word mapped to the selected source word in the source sentence; and correcting a target word, which is generated by translating the source sentence, based on location information of a feature value of the target word and the mapping information.

An electronic device includes a processor and a memory to store a dictionary and index information. The memory can store instructions to cause the processor to: extract a first text in a first language included in data; determine whether or not a transliteration pair is included in the dictionary; if the transliteration pair is identified, index the first text and at least one second text to the data and store same in the index information; and, if the transliteration pair is not identified, extract at least one third text in the second language by inputting the extracted first text to a machine learning model for transliterating an input in the first language into the second language and extracting same, and index the first text and the at least one third text which has been extracted to the data and store same in the index information.

Disclosed herein is an information processing apparatus, comprising: a data set acquisition unit configured to acquire a first training data set that stores training data in which a first natural language sequence is correlated to a second natural language sequence; an analysis unit configured to extract a segment indicating either non-honorific or honorific expressions from the second natural language sequence in the first training data set, and analyze the extracted segment; and a data set conversion unit configured to convert, based on an analysis result of the segment, the first training data set into a second training data set that is different from the first training data set and in which the honorific expression in the second natural language sequence is enriched over the non-honorific expression with respect to the first training data set.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adapting a language model are disclosed. In one aspect, a method includes the actions of receiving transcriptions of utterances that were received by computing devices operating in a domain and that are in a source language. The actions further include generating translated transcriptions of the transcriptions of the utterances in a target language. The actions further include receiving a language model for the target language. The actions further include biasing the language model for the target language by increasing the likelihood of the language model selecting terms included in the translated transcriptions. The actions further include generating a transcription of an utterance in the target language using the biased language model and while operating in the domain.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adapting a language model are disclosed. In one aspect, a method includes the actions of receiving transcriptions of utterances that were received by computing devices operating in a domain and that are in a source language. The actions further include generating translated transcriptions of the transcriptions of the utterances in a target language. The actions further include receiving a language model for the target language. The actions further include biasing the language model for the target language by increasing the likelihood of the language model selecting terms included in the translated transcriptions. The actions further include generating a transcription of an utterance in the target language using the biased language model and while operating in the domain.

A translation method, implemented by a computer, includes: converting a text written in a first language into a replacement text in which a named entity in the text is replaced with a predetermined character string; translating the replacement text into a second language by using a text translation model which is a neural network; and translating a named entity corresponding to the predetermined character string in the replacement text into the second language by using a named entity translation model which is a neural network.

A computer generates a plurality of encoded sentences in the first language by encoding a plurality of sentences in the first language in prescribed units. Next, the computer generates a plurality of encoded sentences in the second language by encoding a plurality of sentences in the second language, each of which is associated with each of the plurality of sentences in the first language, in the prescribed units. The computer generates alignment information based on a code included in each of the plurality of encoded sentences in the first language and a code included in an encoded sentence in the second language, which is associated with each of the plurality of encoded sentences in the first language. The alignment information indicates an alignment between a plurality of codes in the first language and a plurality of codes in the second language.