| CPC G06F 40/284 (2020.01) [G06F 40/295 (2020.01); G06N 3/045 (2023.01); G06F 40/279 (2020.01)] | 20 Claims |
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1. A method performed by one or more computers, the method comprising:
obtaining a conversation transcript sequence comprising a sequence of text tokens from a conversation between two or more participants;
processing the conversation transcript sequence using a span detection neural network configured to:
process the conversation transcript sequence to generate a respective feature representation for each of the text tokens in the sequence; and
process the respective feature representations to generate a set of text token spans, each text token span comprising one or more consecutive text tokens in the sequence of text tokens that references an entity of a particular type;
for each text token span:
generating a span representation from the respective feature representations for the consecutive text tokens in the text token span;
processing the span representation using an entity name neural network to generate an entity name probability distribution over a set of entity names, each probability in the entity name probability distribution representing a likelihood that a corresponding entity name is a name of the entity referenced by the text token span, wherein the entity name neural network comprises an entity name softmax layer configured to:
generate, from the respective feature representations, a respective logit for each entity name; and
generate, from the respective logits for each of the entity names, the entity name probability distribution; and
processing the span representation using an entity status neural network to generate an entity status probability distribution over a set of entity statuses, each probability in the entity status probability distribution representing a likelihood that a corresponding entity status is a status of the entity referenced by the text token span.
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