Systems and methods for neural machine translation are provided. In one example, a neural machine translation system translates text and comprises processors and a memory storing instructions that, when executed by at least one processor among the processors, cause the system to perform operations comprising, at least, obtaining a text as an input to a neural network system, supplementing the input text with meta information as an extra input to the neural network system, and delivering an output of the neural network system to a user as a translation of the input text, leveraging the meta information for translation.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A neural machine translation system for translating text, the system comprising: processors; and a memory storing instructions that, when executed by at least one processor among the processors, cause the system to perform operations comprising, at least: obtaining a text as an input to a neural network system; supplementing the input text with meta information as an extra input to the neural network system; and delivering an output of the neural network system to a user as a translation of the input text, leveraging the meta information for translation. 2. The neural machine translation system of claim 1, wherein the meta information comprises a product category or topic of an e-commerce listing. 3. The neural machine translation system of claim 1, wherein the neural machine translation system includes an encoder-decoder architecture in which an input sentence is first encoded into a fixed-length representation and from which the decoder generates target words. 4. The neural machine translation system of claim 1, wherein the system is trained with an attention mechanism that provides a soft alignment to source sentence words at each decoder step. 5. The neural machine translation system of claim 1, wherein the operations further comprise merging information from a last recurrent neural network decoder state, a last decoded word embedding, a current context or attention vector, and a vector representation of the input-specific meta-information to generate at least part of the output to the user. 6. The neural machine translation system of claim 1, wherein the operations further comprise training the neural network system using, individually or in combination, a training algorithm that utilizes meta-information associated with one of a training pair of a source language sentence and an associated target language human translation, and a document or corpus of such training pairs. 7. A method, at a neural machine translation system, comprising: obtaining a text as an input to a neural network system; supplementing the input text with meta information as an extra input to the neural network system; and delivering an output of the neural network system to a user as a translation of the input text, leveraging the meta information for translation. 8. The method of claim 7, wherein the meta information comprises a product category or topic of an e-commerce listing. 9. The method of claim 7, further comprising encoding an input sentence into a fixed-length representation and generating target words. 10. The method of claim 7, further comprising training the neural machine translation system with an attention mechanism to provide a soft alignment to source sentence words at one or more decoder operations. 11. The method of claim 7, further comprising merging information from a last recurrent neural network decoder state, a last decoded word embedding, a current context or attention vector, and a vector representation of the input-specific meta-information to generate at least part of the output to the user. 12. The method of claim 7, further comprising training the neural machine translation system using, individually or in combination, a training algorithm that utilizes meta-information associated with one or more of a training pair of a source language sentence and an associated target language human translation, and a document or corpus of such training pairs. 13. A non-transitory machine-readable medium including instructions, which when read by a machine, cause the machine to perform operations comprising, at least: obtaining a text as an input to a neural network system; supplementing the input text with meta information as an extra input to the neural network system; and delivering an output of the neural network system to a user as a translation of the input text, leveraging the meta information for translation. 14. The medium of claim 13, wherein the meta information comprises a product category or topic of an e-commerce listing. 15. The medium of claim 13, wherein the operations further comprise encoding an input sentence into a fixed-length representation and generating target words. 16. The medium of claim 13, wherein the operations further comprise training a neural machine translation system with an attention mechanism to provide a soft alignment to source sentence words at one or more decoder operations. 17. The medium of claim 13, wherein the operations further comprise merging information from a last recurrent neural network decoder state, a last decoded word embedding, a current context or attention vector, and a vector representation of the input-specific meta-information to generate at least part of the output to the user. 18. The medium of claim 13, wherein the operations further comprise training the neural machine translation system using, individually or in combination, a training algorithm that utilizes meta-information associated with one or more of a training pair of a source language sentence and an associated target language human translation, and a document or corpus of such training pairs.