1. A system comprising: a machine-language-based classifier, wherein the machine-language-based classifier is configured to perform the following actions: receive a first user input requesting a digital assistant to perform a requested task; identify a first context for the user; analyze the first user input to determine first intermediate task data corresponding to the requested first task; provide the first context and the first intermediate task data as inputs to a machine learned language prediction model, wherein the machine learned language prediction model is based on log data, the log data comprising historical data representing previous interactions between one or more users and one or more digital assistant applications; receive as output from the machine learned language prediction model, first intermediate suggestion data for generating a first suggestion for the user, wherein the first suggestion is for a second task to be requested based on the inputs to the machine learned language prediction model; receive a second user input, wherein the second user input is the same as the first user input; receive a second context for the user, wherein the second context is different from the first context; analyze the second user input to determine second intermediate task data corresponding to the requested task; provide the second context and the second intermediate task data as inputs to the machine learned language prediction model; and receive as output from the machine learned language prediction model, second intermediate suggestion data for generating a second suggestion for the user, wherein the second suggestion is different from the first suggestion; and an output module, wherein the output module is configured to present the first suggestion and the second suggestion to the user.
3. The system of claim 1 , further comprising a language generation suggestion module configured to generate a surface form of the first suggestion based on the first intermediate suggestion data.