Learning to extract entities from conversations with neural networks

One or more non-transitory computer-readable media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations 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.

A system comprising one or more computers and one or more storage devices storing instructions that when executed by one or more computers cause the one or more computers to perform operations 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.