Clinc, Inc.

Clinic, Inc.

Anish Mahendran

Jason Mars

Lingjia Tang

Michael A Laurenzano

Parker Hill

Jonathan K Kummerfeld

Stefan Larson

Johann Hauswald

Andrew Lee

Systems and methods for automatically detecting annotation discrepancies in annotated training data samples and repairing the annotated training data samples for a machine learning-based automated dialogue system include evaluating a corpus of a plurality of distinct training data samples; identifying one or more of a slot span defect and a slot label defect of a target annotated slot span of a target training data sample of the corpus based on the evaluation; and automatically correcting one or more annotations of the target annotated slot span based on the identified one or more of the slot span defect and the slot label defect.

Systems and methods for automatically detecting and repairing slot errors in machine learning training data for a machine learning-based dialogue system

A system and method for improving a machine learning-based dialogue system includes: sourcing a corpus of raw machine learning training data from sources of training data based on a plurality of seed training samples, wherein the corpus of raw machine learning training data comprises a plurality of distinct instances of training data; generating a vector representation for each distinct instance of training data; identifying statistical characteristics of the corpus of raw machine learning training data based on a mapping of the vector representation for each distinct instance of training data; identifying anomalous instances of the plurality of distinct instances of training data of the corpus of raw machine learning training data based on the identified statistical characteristics of the corpus; and curating the corpus of raw machine learning training data based on each of the instances of training data identified as anomalous instances.

Systems and methods for automatically configuring training data for training machine learning models of a machine learning-based dialogue system including seeding training samples or curating a corpus of training data based on instances of training data identified as anomalous

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Ann Arbor, MI, United States of America

Anish Mahendran