Determining Intent from Unstructured Input to Update Heterogeneous Data Stores

A neural network for determining an intent associated with an unstructured text input sequence, the neural network comprising:

• a pre-processing layer configured to receive the unstructured text input sequence, wherein the unstructured text input sequence comprises a plurality of words, wherein at least a portion of the unstructured text input sequence relates to an action item to be taken with respect to modifying a database, the pre-processing layer configured to generate an embedding for each word in the unstructured text input sequence;
• an encoder stack comprising a plurality of encoding layers, each of the encoding layers configured to generate encodings for the embeddings;
• a softmax layer configured to generate, based at least in part on the encodings, a probable classification for the intent associated with the unstructured text input sequence regarding an action item to be taken with respect to modifying the database;
• a fully connected layer configured to provide weights for determining the probable classification;
• and
• a bypass path;
• wherein the neural network is operable to be trained on a plurality of training data sets, the fully connected layer is configured to determine which features in a given training data set correlate to a particular classification, and the bypass path is configured to bypass the fully connected layer for some training data sets.