Data input system using trained keypress encoder

A computer-implemented data input method;

• the method comprising:
• determining, by a computing device, user input events based on a text sequence input received via a virtual keyboard, the text sequence including at least one context word and at least one partial word;
• identifying keypress evidence for one or more characters in the at least one partial word of the text sequence, the keypress evidence comprising one or more probabilities that a plurality of the user input events correspond to each of a plurality of possible characters or keyboard functions;
• encoding the keypress evidence into a numerical encoding using a trained keypress encoder, the trained keypress encoder trained using previous keypress evidence and corresponding words, wherein a portion of the numerical encoding corresponds to the at least one partial word;
• generating, by a trained context encoder, an embedding of one or more candidate text items using a portion of the numerical encoding corresponding to the at least one context word of the text sequence;
• and
• computing a text item in the text sequence corresponding to the at least one partial word, using a neural network, based on inputs to the neural network including a concatenated vector formed from the portion of the numerical encoding corresponding to the at least one partial word and the embedding of the one or more candidate text items.

A computer-readable storage medium storing thereon computer-executable instructions that, when executed by a computing system, cause the computing system to perform operations comprising:

• receiving, via a virtual keyboard, user input events indicative of a text sequence;
• computing keypress evidence comprising one or more probabilities that the user input events correspond to characters or keyboard functions;
• encoding the keypress evidence into a numerical encoding using a trained keypress encoder comprising a neural network, wherein a portion of the numerical encoding corresponds to the at least one partial word;
• and
• computing a text item in the text sequence corresponding to the at least one partial word, using a second neural network, based on inputs to the neural network including a concatenated vector formed from the portion of the numerical encoding corresponding to the at least one partial word and an encoding of one or more text items of the text sequence already input to the computing system.