Training systems and methods for sequence taggers

A method for training a sequence tagger utilizing machine learning techniques, the method comprising:

• obtaining partially labeled data from a first source for a specific application;
• obtaining partially labeled data from a second source, wherein the second source is search logs;
• merging the partially labeled data from the first source and from the search logs into a constrained lattice,
• wherein each input value within the constrained lattice has a plurality of candidate tags with confidence scores, and
• running a training algorithm based on the constrained lattice to estimate model parameters,
• wherein the method provides for a more accurate sequence tagger and a more reliable sequence tagger in comparison to sequence taggers that are trained with at least some fully-labeled data.

A system for building a language understanding model utilizing machine learning techniques, the system comprising:

• at least one processor;
• and
• one or more system memories including computer-executable instructions stored thereon that, responsive to execution by the at least one processor, cause the system to perform operations including:
• obtaining partially labeled data from crowd-sourced data for a specific application;
• obtaining partially labeled data from search logs;
• merging the partially labeled data from the crowd-sourced data and from the search logs into a constrained lattice,
• wherein each word within the constrained lattice has a plurality of candidate tags with confidence scores, and
• wherein the constrained lattice is constrained because every word has a set of allowed candidate tag types and because the plurality of candidate tags is structured;
• and
• running a training algorithm based on the constrained lattice to estimate model parameters,
• wherein the language understanding model is a trained conditional random field.