Provided is a method for user training of an information dialogue system. The method may include activating a user input subsystem, receiving a training request entered by the user, converting the training request into text by the user input subsystem, sending the text of the training request obtained as a result of the conversion to a dialogue module, processing the text of the training request by the dialogue module, forming a response to the training request by the dialogue module, and sending the response to the training request to the user. The response to the training request may be formed in a form of one or more of the following: a voice cue, a text, and an action performed by the information dialogue system.

Method for user training of information dialogue system

A user is allowed to communicate with a chatbot. A menu is provided to the user that includes a list of actions that can be performed by the user. Whenever natural language input asking a question is received from the user, this input is forwarded to the chatbot, a response to this input is received from the chatbot, this response is provided to the user, and the menu is again provided to the user. Whenever natural language input is received from the user requesting an action that is not one of the actions in the menu, this input is forwarded to the chatbot, a response to this input is received from the chatbot, where this response includes another menu that includes a list of subsequent actions that are related to the requested action and can be performed by the user, and this other menu is provided to the user.

Combined menu-based and natural-language-based communication with chatbots

A server sends, to an electronic device of a first user, a data structure that includes a hierarchical interaction tree having a plurality of interaction nodes. The server then receives, from the electronic device of the first user, a first message, wherein the first message from the electronic device of the first user is selected from a set of predefined first-reply messages. Each first-reply message of the set of predefined first-reply messages corresponds to a distinct interaction node of the hierarchical interaction tree.

Device, Method, and Graphical User Interface for Managing Customer Relationships using a Lightweight Messaging Platform

An electronic device of a second user receives, from a first user, a first message. Subsequent to receiving the first message, the electronic device sends, to the first user, a second message responsive to the first message, wherein the second message is selected from a set of predefined first-reply messages. The set of predefined first-reply messages is based on first-reply factors for the second user, including content from one or more messages between the first user and the second user, and including one or more of: relationship characteristics between the first user and the second user, information provided by the first user and/or the second user to a social network, profile characteristics of the first user and/or the second user, environmental characteristics of the first user and/or the second user, and/or technical characteristics of the electronic device of the first user and/or the second user.

Device, Method, and Graphical User Interface for Lightweight Messaging

Examples are generally directed towards automatic assessment of machine generated conversational responses. Context-message-response n-tuples are extracted from at least one source of conversational data to generate a set of multi-reference responses. A response in the set of multi-reference responses includes it context-message data pair and rating. The rating indicates a quality of the response relative to the context-message data pair. A response assessment engine generates a metric score for a machine-generated response based on an assessment metric and the set of multi-reference responses. The metric score indicates a quality of the machine-generated conversational response relative to a user-generated message and a context of the user-generated message. A response generation system of a computing device, such as a digital assistant, is optimized and adjusted based on the metric score to improve the accuracy, quality, and relevance of responses output to the user.

Metric for automatic assessment of conversational responses

A candidate message chatbot system and method. The chatbot includes an interactive dialog interface for engaging in a chat session with a user. The user can enter one or more characters as an input message during the chat session. The chatbot can match the one or more characters with a plurality of candidate messages in a knowledge database, the plurality of candidate input messages being part of input/output knowledge entry or known to generate at least a quality response.

Xiaojiang Duan

Papers

Chatbot system and method having auto-select input message with quality response