Abstract: Systems and processes for multi-modal input interpretation are provided. For example, an input associated with a touch is received from a user. A first reconstruction based on the input is determined. A first simulated input is obtained based on a modification of the input. A second reconstruction is determined based on the first reconstruction and the first simulated input. Based on at least the first reconstruction and the second reconstruction, a probability representation is obtained. An output is determined, by a language model, based on the probability representation. The output is then provided to the user.

Abstract: An example process includes: obtaining input token(s); determining, using a joint prediction model, based on the input token(s): a first predicted token following the input token(s) and a second predicted token following the first predicted token; and a first user action to be performed on the first predicted token, where determining the first user action includes: determining a first reward value for performing the first user action based on a first current reward value for performing the first user action and a second reward value for performing a second user action on the second predicted token; outputting the first predicted token; detecting a user action performed on the first predicted token; and in accordance with a determination that the detected user action does not match the first user action: causing parameters of the joint prediction model to be updated, the parameters being configured to determine the first user action.

Abstract: An example method includes at an electronic device with one or more processors and memory: obtaining first data representing a user-generated keyboard path for one or more words; obtaining second data representing a synthetic keyboard path for the one or more words; generating, using a first instance of a generative network, based on the first data and the second data, third data representing a modification of the synthetic keyboard path; determining whether the third data represent a second user-generated keyboard path; determining whether the third data represent the one or more words; and in accordance with a determination that the third data represent a second user-generated keyboard path and a determination that the third data represent the one or more words: training a model for keyboard path recognition based on the third data.

Abstract: Methods, systems, and computer-readable media for mapping entities to social profile data. Social data regarding an entity (e.g., a notable entity, celebrity, movie, famous brand, etc.) is filtered from one or more open knowledge databases to produce filtered social data regarding the entity (or entities). The filtered social data is clustered and classified with respect to the entity according to a hash function to produce candidate results related to the entity. Ambiguous results are then filtered out from the candidate results, thereby automatically mapping facts contained in one or more of the open knowledge databases to a social profile associated with the entity.

Abstract: Methods, systems, and computer-readable media for mapping entities to social profile data. Social data regarding an entity (e.g., a notable entity, celebrity, movie, famous brand, etc.) is filtered from one or more open knowledge databases to produce filtered social data regarding the entity (or entities). The filtered social data is clustered and classified with respect to the entity according to a hash function to produce candidate results related to the entity. Ambiguous results are then filtered out from the candidate results, thereby automatically mapping facts contained in one or more of the open knowledge databases to a social profile associated with the entity.

Abstract: Methods, systems, and computer-readable media for mapping entities to social profile data. Social data regarding an entity (e.g., a notable entity, celebrity, movie, famous brand, etc.) is filtered from one or more open knowledge databases to produce filtered social data regarding the entity (or entities). The filtered social data is clustered and classified with respect to the entity according to a hash function to produce candidate results related to the entity. Ambiguous results are then filtered out from the candidate results, thereby automatically mapping facts contained in one or more of the open knowledge databases to a social profile associated with the entity.

Abstract: Methods, systems, and computer-readable media for mapping entities to social profile data. Social data regarding an entity (e.g., a notable entity, celebrity, movie, famous brand, etc.) is filtered from one or more open knowledge databases to produce filtered social data regarding the entity (or entities). The filtered social data is clustered and classified with respect to the entity according to a hash function to produce candidate results related to the entity. Ambiguous results are then filtered out from the candidate results, thereby automatically mapping facts contained in one or more of the open knowledge databases to a social profile associated with the entity.