Utterance intent detection

A system comprising:

• a memory comprising computer-readable instructions;
• a processor configured to read the computer-readable instructions, that cause the system to:
• receive a plurality of corpus sentences, wherein each corpus sentence of the plurality of corpus sentences comprises a sentence from a respective transcript of previous interactions involving a respective agent;
• receive a plurality of query sentences, wherein each query sentence of the plurality of query sentences is associated with an intent to poach a customer;
• determine a query sentence embedding for each query sentence of the plurality of query sentences;
• determine a corpus sentence embedding for each corpus sentence of the plurality of corpus sentences, based on one or more query sentence embeddings of the plurality of query sentences;
• determine a semantic similarity score for at least one corpus sentence embedding relative to at least one query sentence embedding of the plurality of query sentences;
• identify one corpus sentence as a base sentence, based on the semantic similarity score associated with the corpus sentence embedding of the corpus sentence being at or above a threshold;
• receive a transcript sentence;
• provide the transcript sentence to a machine learning model, wherein the machine learning model has been trained through a supervised learning process using training data that includes the base sentence to determine a transcript similarity score for the transcript sentence, based on the base sentence;
• receive a transcript similarity score for the transcript sentence from the machine learning model;
• detect the intent in the transcript sentence based on the transcript similarity score exceeding a threshold;
• and
• provide an indicator of the transcript similarity score to a user device.

A method comprising:

• receiving a base sentence embedding, wherein the base sentence embedding is generated for a base sentence, wherein the base sentence embedding is used to identify the base sentence as semantically similar to a sentence that is associated with an intent to poach a customer;
• receiving agent content;
• generating an agent content embedding from the agent content;
• generating a similarity score between the base sentence embedding and the agent content embedding using a machine learning model that has been trained through a supervised learning process using training data that includes the base sentence;
• and
• identifying the intent and providing an output to a user based on the similarity score exceeding a given threshold.