Abstract: A chat input identifier may receive various chat inputs based on voice or text inputs from a user. The chat input identifier may apply different filters to the chat inputs to identify one or more chat correction pairs (e.g., chat input with errors, corrected chat input) from among the plurality of chat inputs. The chat correction pairs are used to train an auto-correction model. The trained auto-correction model receives a given chat input that has one or more errors. The auto-correction model processes the given chat input to generate a corrected version of the given chat input (without the need to obtain a correction from the user). The corrected chat input is then provided to a dialog-driven application.

Abstract: A chat input identifier may receive various chat inputs based on voice or text inputs from a user. The chat input identifier may apply different filters to the chat inputs to identify one or more chat correction pairs (e.g., chat input with errors, corrected chat input) from among the plurality of chat inputs. The chat correction pairs are used to train an auto-correction model. The trained auto-correction model receives a given chat input that has one or more errors. The auto-correction model processes the given chat input to generate a corrected version of the given chat input (without the need to obtain a correction from the user). The corrected chat input is then provided to a dialog-driven application.

Abstract: Systems and methods for transforming formal and informal natural language user inputs into a more formal, machine-readable, structured representation of a search query. In one scenario, a processed sequence of user inputs and machine-generated prompts for further data from a user in a multi-turn interactive dialog improves the efficiency and accuracy of automated searches for the most relevant items available for purchase in an electronic marketplace. Analysis of user inputs may discern user intent, user input type, a dominant object of user interest, item categories, item attributes, attribute values, and item recipients. Other inputs considered may include dialog context, item inventory-related information, and external knowledge to improve inference of user intent from user input. Different types of analyses of the inputs each yield results that are interpreted in aggregate and coordinated via a knowledge graph based on past users' interactions with the electronic marketplace and/or inventory-related data.