A system and method for implementing a machine learning-based virtual dialogue agent includes computing an input embedding based on receiving a user input; computing, via a pre-trained machine learning language model, an embedding response inference based on the input embedding; searching, based on the embedding response inference, a response imprintation embedding space that includes a plurality of distinct embedding representations of potential text-based responses to the user input, wherein each of the plurality of distinct embedding representations is tethered to a distinct human-imprinted media response, and searching the response imprintation embedding space includes: searching the response imprintation embedding space based on an embedding search query, and returning a target embedding representation from the response imprintation embedding space based on the searching of the response imprintation embedding space; and executing, via a user interface of the machine learning-based virtual dialogue agent, a human-imprinted media response tethered to the target embedding representation.

In implementations of the subject matter described herein, a new approach for presenting a response to a message in a conversation is proposed. Generally speaking, in response to receiving a message in a conversation, the received message will be matched with one or more documents on the sentence basis. That is, the received message is compared with the sentences from a document(s), rather than predefined query-response pairs. In this way, a whole sentence may be selected from the document as a candidate response. Then the suitability of this sentence with respect to the ongoing conversation will be determined, and the response will be generated and rendered in an adaptive way based on the suitability. As a result, the user experiences may be significantly enhanced in the chatbot scenario.

Systems, devices, and methods of the present invention calculate a rhetorical relationship between one or more sentences. In an example, a computer-implemented method accesses a sentence comprising a plurality of fragments. At least one fragment includes a verb and a words. Each word includes a role of the words within the fragment. Each fragment is an elementary discourse unit. The method generates a discourse tree that represents rhetorical relationships between the sentence fragments. The discourse tree includes nodes including nonterminal and terminal nodes, each nonterminal node representing a rhetorical relationship between two of the sentence fragments, and each terminal node of the nodes of the discourse tree is associated with one of the sentence fragments. The method matches each fragment that has a verb to a verb signature, thereby creating communicative discourse tree.

One embodiment provides a method, including: utilizing at least one processor to execute computer code that performs the steps of: providing, on a display device, a communications window, wherein the communications window comprises a request for user input to start a conversation with an online assistant; receiving a user input identifying a request by the user to be completed by the online assistant; updating, based upon the request, the communications window, wherein the updated communications window comprises a summary of the conversation including prepopulated variable terms and allows user interaction to adjust the summary including adjustment of the prepopulated variable terms; and iteratively updating the communications windows based upon user input adjusting the summary. Other aspects are described and claimed.

In one embodiment, a method includes receiving an initial input in a first modality from a first user from a client system associated with the first user, determining one or more intents corresponding to the initial input by an intent-understanding module, generating one or more candidate continuation-inputs based on the one or more intents, where the one or more candidate continuation-inputs are in one or more candidate modalities, respectively, and wherein the candidate modalities are different from the first modality, and sending instructions for presenting one or more suggested inputs corresponding to one or more of the candidate continuation-inputs to the client system.

This disclosure is provided, in which an answer generation unit configured to receive a document and a question as inputs, and execute processing of generating an answer sentence for the question by a learned model by using a word included in a union of a predetermined first vocabulary and a second vocabulary composed of words included in the document and the question, in which the learned model includes a learned neural network that has been learned in advance whether word included in the answer sentence is included in the second vocabulary, and increases or decreases a probability at which a word included in the second vocabulary is selected as the word included in the answer sentence at the time of generating the answer sentence by the learned neural network.

A method comprises receiving a natural language search query, identifying a first set of semantic concepts in the query, creating a vector representation of the first set of semantic concepts, identifying a second set of semantic concepts having a vector representation within a predetermined threshold of similarity to the first set of semantic concepts, performing a search of documents based on the first set of semantic concepts, presenting a result set of documents and the first, second, and third sets of semantic concepts to a user, receiving input from the user, performing a second search of the documents based on the input from the user to obtain a second result set of documents, identifying a fourth set of semantic concepts based on the second result set of documents, and presenting the second result set of documents and the fourth set of semantic concepts to the user.

In one embodiment, a method includes receiving a user request from a client system associated with a user, generating a response to the user request which references one or more entities, generating a personalized recommendation based on the user request and the response, wherein the personalized recommendation references one or more of the entities of the response, and sending instructions for presenting the response and the personalized recommendation to the client system.

A response system includes an acquisition unit configured to acquire the question sentence of a user, a response sentence selection unit configured to select a response sentence, stored in a storage unit in advance, according to the acquired question sentence, a response sentence conversion unit configured to convert the selected response sentence according to the question sentence, and an output unit configured to output the converted response sentence. The response sentence conversion unit is configured to convert a word included in the selected response sentence to a word included in the question sentence based on the degree of similarity between the word included in the response sentence and the word included in the question sentence.

A computer-implemented method of performing an incremental specification of a query includes extracting text from each of a plurality of participants in a dialog. A contextual information is determined of the extracted text of one or more of the plurality of participants. A dialog understanding operation is performed by processing at least the contextual information of the extracted text in a knowledge graph to identify in the dialog at least one or more of a structural gap, an information about entities, relationships, and actions. Query information is provided responsive to the dialog for at least one of filling the identified structural gap, or for providing additional information about one or more of the identified entities, relationships or actions in the dialog.