Selectively generating expanded responses that guide continuance of a human-to-computer dialog

A method implemented by one or more processors, the method comprising:

• determining at least one entity of interest, wherein determining the at least one entity of interest is based on user interface input of a user, the user interface input provided via a client device during a human-to-computer dialog session that is facilitated by the client device and that is between the user and an automated assistant;
• transmitting, to each of a plurality of disparate content agents, a corresponding request that identifies the at least one entity of interest;
• receiving, from each of the disparate content agents, at least one corresponding textual snippet that is responsive to the corresponding request;
• processing the corresponding textual snippets, using a trained machine learning model, to determine a subgroup of the corresponding textual snippets, the subgroup of the corresponding textual snippets including:
• a first textual snippet from a first content agent of the disparate content agents, and
• a second textual snippet from a second content agent of the disparate content agents,
• and the subgroup of the corresponding textual snippets excluding:
• a third textual snippet, the third textual snippet being from the first content agent, the second content agent, or a third content agent of the disparate content agents;
• combining the corresponding textual snippets of the subgroup into a composite response, wherein combining the corresponding textual snippets of the subgroup into the composite response comprises:
• applying tokens of the textual snippets of the subgroup as input to a trained generative model, the trained generative model being a sequence-to-sequence model, and
• generating the composite response over the trained generative model based on the input, wherein the composite response differs from the textual snippets of the subgroup and wherein generating the composite response comprises producing the composite response based on learned parameters of the trained generative model;
• and
• causing the client device to render the composite response, the composite response rendered as a response, from the automated assistant, that is responsive to the user interface input provided during the human-to-computer dialog session.