Systems and methods for automatically configuring training data for training machine learning models of a machine learning-based dialogue system

Abstract

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.

What is claimed: 
     
         1 . A system comprising:
 a machine learning-based automated dialogue service implementing by one or more hardware computing servers that:
 identifies statistical characteristics of a corpus of raw machine learning training data based on a mapping of a vector representation for each instance of training data within the corpus of raw machine learning training data; 
 identifies, as anomalous instances, each of one or more instances of training data of the corpus of raw machine learning training data based on the identified statistical characteristics; and 
 curates the corpus of raw machine learning training data based on each of the one or more instances of training data identified as anomalous instances. 
   
     
     
         2 . A method comprising:
 identifying statistical characteristics of a corpus of raw machine learning training data based on a mapping of a vector representation for each distinct instance of training data within the corpus of raw machine learning training data;   identifying one or more 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 one or more instances of training data identified as anomalous instances.