Abstract: In the present application, a method for providing interactive documentation based on a chatbot and deep-learning based techniques is disclosed. Metadata associated with program code documentation is identified, wherein the program code documentation is associated with corresponding program code. A natural language question regarding the corresponding program code is obtained via a virtual agent. A response to the natural language question is determined based on the metadata using one or more trained machine learning models. The response to the natural language question is provided to the virtual agent.

Abstract: A pre-trained natural language processing machine learning model is received. The pre-trained natural language processing machine learning model is tuned. A workflow to be implemented by a chatbot to complete a task-oriented dialog is received. A representation of one or more plan steps based on a current progression state of the workflow is dynamically determined. At least the representation of the one or more plan steps is provided as an input to the pre-trained natural language processing machine learning model to guide the pre-trained natural language processing machine learning model. An output action and/or an utterance for the task-oriented dialog is received from the pre-trained natural language processing machine learning model.

Abstract: An indication to predict one or more additional steps to be added to a partially specified computerized workflow based at least in part on the partially specified computerized workflow is received. Text descriptive of at least a portion of the partially specified computerized workflow is generated. Machine learning inputs based at least in part on the descriptive text are provided to a machine learning model to determine an output text descriptive of the one or more additional steps to be added. One or more processors are used to automatically implement the one or more additional steps to be added to the partially specified computerized workflow.

Abstract: A user provided text description of at least a portion of a desired workflow is received. Context information associated with the desired workflow is determined. Machine learning inputs based at least in part on the text description and the context information are provided to a machine learning model to determine an implementation prediction for the desired workflow. One or more processors are used to automatically implement the implementation prediction as a computerized workflow implementation of at least a portion of the desired workflow.

Abstract: Content of a dialog between at least two communication parties to resolve a task is received. A specification associated with at least a portion of eligible steps of a workflow is received. Machine learning input data is determined based on the received content of the dialog and the received specification. The determined machine learning input data is processed using a trained machine learning model executing on one or more hardware processors to automatically predict a sequence of workflow steps representing the dialog.

Abstract: Systems and methods relating to neural networks. More specifically, the present invention relates to complex valued gating mechanisms which may be used as neurons in a neural network. A novel complex gated recurrent unit and a novel complex recurrent unit use real values for amplitude normalization to stabilize training while retaining phase information.