System and method with neural representation of event-centric commonsense knowledge for response selection

A system comprising:

• at least one non-transitory computer readable medium including computer readable data;
• and
• a processor operably connected to the at least one non-transitory computer readable medium, the processor being configured to execute the computer readable data to perform a method that includes:
• receiving input data;
• obtaining situational data that provides context for the input data;
• creating a first dataset that includes concatenated data, the concatenated data including at least a concatenation of the input data and the situational data;
• generating, via an encoder, an encoded representation of the first dataset, the encoder including (i) a token embedder and (ii) an encoding network of a first pre-trained generative machine learning model that relates to a generative knowledge base, the token embedder being configured in parallel with the encoding network such that the token embedder and the encoding network receive the same concatenated data as input;
• generating, via a decoder, response data based on the first dataset by decoding the encoded representation, the decoder including a decoding network of a second pre-trained generative machine learning model that includes a first language model head and a second language model head;
• and
• generating, via the decoder, goal data based on the first dataset by decoding the encoded representation,
• wherein
• the input data and the response data are connected to the goal data via the generative knowledge base,
• the first language model head generates the response data, and
• the second language model head generates the goal data.

A computer-implemented method comprising:

• receiving input data;
• obtaining situational data that provides context for the input data;
• generating concatenated data, the concatenated data including at least a concatenation of the input data and the situational data;
• obtaining a set of candidate responses;
• generating, via an encoder, an encoded representation of the input data and the situational data, the encoder including (i) a token embedder and (ii) an encoding network of a first pre-trained generative machine learning model that relates to a generative knowledge graph, the token embedder being configured in parallel with the encoding network such that the token embedder and the encoding network receive the same concatenated data as input:
• generating, via a decoder, goal data by decoding the encoded representation of the input data and the situational data, the decoder including a decoding network of a second pre-trained generative machine learning model that includes a first language model head and a second language model head;
• and
• generating, via the decoder, a likelihood score for each candidate response of the set of candidate responses based on the input data and the situational data,
• wherein,
• the first language model head generates the likelihood score data, and
• the second language model head generates the goal data.