Intuit, Inc.

Sudhir Agarwal

Saikat Mukherjee

Karpaga Ganesh Patchirajan

Esmé Manandise

Lalla Mouatadid

Roland Allen Sanford, Ii

Anu Sreepathy

Systems and methods for lean parsing are disclosed. An example method is performed by one or more processors of a system and includes retrieving form data including first sentence segments and second sentence segments, determining a first predicate structure for each of the sentence segments based on a set of operators within the first set of sentence segments, identifying known tokens within the second set of sentence segments, each of the known tokens appearing on a list of predetermined tokens, identifying new tokens within the second set of sentence segments, each of the new tokens not on the list, mapping each known and new token to at least one operator, determining a second predicate structure for each sentence segment based on the mapping, and generating a predicate argument structure incorporating the first and second predicate structures, the predicate argument structure ready for mapping to at least one machine executable function.

Lean parsing: a natural language processing system and method for parsing domain-specific languages

Systems and methods for training machine learning models based on domain constraints are disclosed. An example method includes receiving a plurality of images, each image associated with a cluster of a plurality of clusters, the plurality of clusters representing an output of a second machine learning model, assigning a label to each cluster of the plurality of clusters based at least in part on a plurality of constraints, identifying, based at least in part on the plurality of constraints, a first label mismatch for a first image, the first label mismatch indicating that the first image belongs to a first cluster but should be assigned to a second cluster different from the first cluster, reassigning the first image to the second cluster, and training the first machine learning model, based on the labeled clusters of the plurality of clusters, to predict labels associated with subsequently received image data.

Scalable weak-supervised learning with domain constraints

A method and system parses natural language in a unique way, determining important words pertaining to a text corpus of a particular genre, such as tax preparation. Sentences extracted from instructions or forms pertaining to tax preparation, for example are parsed to determine word groups forming various parts of speech, and then are processed to exclude words on an exclusion list and word groups that don't meet predetermined criteria. From the resulting data, synonyms are replaced with a common functional operator and the resulting sentence text is analyzed against predetermined patterns to determine one or more functions to be used in a document preparation system.

Certain aspects of the present disclosure provide techniques for hierarchical optimization including: receiving a request to optimize a primary objective; determining a set of stages to optimize the primary objective; for each respective stage of the set of stages: determining an objective function; when the respective stage is the first stage to be processed: determining values of a set of variable inputs to the respective stage and an output of the objective function; when the respective stage is not the first stage: updating a set of fixed inputs to the respective stage by including the variable inputs to one or more previously processed stages and their corresponding values to the set of fixed inputs to the respective stage; determining values of the set of variable inputs to the respective stage and output of the objective function for the respective stage; providing a final output for display on a display device.

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Palo Alto, CA, United States of America

Sudhir Agarwal