Abstract: In an example embodiment, a software application is introduced that is able to automatically detect whether a conversation in a chat interface is with a human or an artificial intelligence. More specifically, the software application is able to identify how the chat interface is interacted with and replicate that mechanism to allow the software application to directly contact the other party (whether human or AI) on the other side of a chat conversation.

Abstract: In an example embodiment, a software application is introduced that is able to automatically detect whether a conversation in a chat interface is with a human or an artificial intelligence. More specifically, the software application is able to identify how the chat interface is interacted with and replicate that mechanism to allow the software application to directly contact the other party (whether human or AI) on the other side of a chat conversation.

Abstract: A method may include training a first machine learning model to perform a question generation task and a second machine learning model to perform a question answering task. The first machine learning model and the second machine learning model may be subj ected to a collaborative training in which a first plurality of weights applied by the first machine learning model generating one or more questions are adjusted to minimize an error in an output of the second machine learning model answering the one or more questions. The first machine learning model and the second machine learning model may be deployed to perform a natural language processing task that requires the first machine learning model to generate a question and/or the second machine learning model to answer a question. Related methods and articles of manufacture are also disclosed.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for efficient use of word embeddings for text classification. A text classification system receives a message including a keyword and determines an embedding value for the keyword. The text classification system uses the embedding value as input into each mathematical function in a set mathematical functions, yielding a first set of coefficient values for the keyword. Each respective mathematical function corresponds to a respective intent and defines a continuous surface determined from a subset of coefficient values and embedding values for a set of known keywords. For each intent, the text classification system calculates a probability score based on the respective coefficient value from the set of coefficient values that corresponds to the respective intent, yielding a set of probability scores for the message, and the assigns an intent to the message based on the set of probability scores for the message.