Abstract: A computing system performs several acts, where the acts include providing text generated by a generative model and content of a webpage to a computer-implemented text rewriting model, where the generative model generated the text based upon user input received from a client computing device, and further where the generative model generated a citation to the webpage to indicate that the text generated by the generative model is supported by the content of the webpage. The acts also include generating, by the computer-implemented text rewriting model, a rewriting of the text, where the computer-implemented text rewriting model generates the rewriting of the text based upon: 1) the text generated by the generative model; and 2) the content of the webpage. The acts further include transmitting the rewriting of the text to the client computing device for presentment as a response to the user input received from the client computing device.

Abstract: A computing system performs several acts, where the acts include providing text generated by a generative model and content of a webpage to a computer-implemented text rewriting model, where the generative model generated the text based upon user input received from a client computing device, and further where the generative model generated a citation to the webpage to indicate that the text generated by the generative model is supported by the content of the webpage. The acts also include generating, by the computer-implemented text rewriting model, a rewriting of the text, where the computer-implemented text rewriting model generates the rewriting of the text based upon: 1) the text generated by the generative model; and 2) the content of the webpage. The acts further include transmitting the rewriting of the text to the client computing device for presentment as a response to the user input received from the client computing device.

Abstract: Methods, systems, and media for storing entries in and/or retrieving information from an object memory are provided. In some examples, a content item is received that has content data. The content data associated with the content item may be provided to one or more semantic models that generate semantic objects. From one or more of the semantic models, one or semantic objects may be received. The one or more semantic objects may then be inserted into the object memory. The semantic objects may be associated with respective indications corresponding to a reference to source data associated with the semantic objects. Further, the insertion may trigger a memory storage operation to store the semantic objects. A plurality of collections of stored objects may be received from the object memory, based on a provided input, to determine a result.

Abstract: Aspects of the present disclosure relate to systems and methods for identifying and providing a passage that is highly convincing in terms of one or more stances for a given topic. A convincingness ranking model is based on a machine learning model, a multi-layer feed forward neural network with a back propagation for updating for example. The query represents a stance under a topic. A convincingness ranking model trainer identifies passage pairs that relate to the query, and labels the passage pairs according to relative levels of convincingness from the stance between the two passages in the respective passage pairs. The trainer provides a user interface to interactively receive selections of the relative levels of convincingness of the passage pairs. The system ranks passages from the passage pairs based on the labels and filter out specific passages when the rank forms a cyclic relationship in a directed graph.