Toxic vector mapping across languages

Abstract

Methods, systems, and devices for language mapping are described. Some machine learning models may be trained to support multiple languages. However, word embedding alignments may be too general to accurately capture the meaning of certain words when mapping different languages into a single reference vector space. To improve the accuracy of vector mapping, a system may implement a supervised learning layer to refine the cross-lingual alignment of particular vectors corresponding to a vocabulary of interest (e.g., toxic language). This supervised learning layer may be trained using a dictionary of toxic words or phrases across the different supported languages in order to learn how to weight an initial vector alignment to more accurately map the meanings behind insults, threats, or other toxic words or phrases between languages. The vector output from this weighted mapping can be sent to supervised models, trained on the reference vector space, to determine toxicity scores.

What is claimed is: 
     
         1 . A method for language mapping on a server, comprising:
 receiving, on the server, a string representing a phrase in a first language;   determining a vector for the string using a word embedding operation for the first language;   mapping the vector to a vector space associated with a reference language;   remapping the vector based at least in part on a set of words translated into both the first language and the reference language for focusing the language mapping; and   outputting, from the server, a result based at least in part on the remapped vector.