Machine learning techniques for dialog-based service order acquisition

A system, comprising:

• one or more computing devices;
• wherein the one or more computing devices include instructions that upon execution on or across the one or more computing devices:
• identify, at a network-accessible service of a cloud computing environment, one or more neural network models which detect references, within unstructured input received from item consumers, to individual ones of a first set of items associated with a first problem domain;
• and
• conduct, at the network-accessible service, a plurality of application enhancement iterations for a particular application associated with the first problem domain, wherein an individual application enhancement iteration comprises:
• obtaining an indication of an additional set of items which is associated with the first problem domain;
• training an iteration-specific version of the one or more neural network models to detect references, within unstructured input, to individual ones of at least the additional set of items, including at least some unstructured input which does not include names of referenced items of the additional set of items;
• and
• deploying the trained iteration-specific version of the one or more neural network models to process unstructured input directed to the particular application.

A computer-implemented method, comprising

• identifying, at a network-accessible service of a cloud computing environment, one or more neural network models which detect references, within unstructured input received from item consumers, to individual ones of a first set of items associated with a first problem domain;
• and
• conducting, at the network-accessible service, a plurality of application enhancement iterations for a particular application associated with the first problem domain, wherein an individual application enhancement iteration comprises:
• obtaining an indication of an additional set of items which is associated with the first problem domain;
• training an iteration-specific version of the one or more neural network models to detect references, within unstructured input, to individual ones of at least the additional set of items, including at least some unstructured input which does not include names of referenced items of the additional set of items;
• and
• deploying the trained iteration-specific version of the one or more neural network models to process unstructured input directed to the particular application.

One or more non-transitory computer-accessible storage media storing program instructions that when executed on or across one or more processors:

• identify, at a network-accessible service of a cloud computing environment, one or more neural network models which detect references, within unstructured input received from item consumers, to individual ones of a first set of items associated with a first problem domain;
• and
• conduct, at the network-accessible service, a plurality of application enhancement iterations for a particular application associated with the first problem domain, wherein an individual application enhancement iteration comprises:
• obtaining an indication of an additional set of items which is associated with the first problem domain;
• training an iteration-specific version of the one or more neural network models to detect references, within unstructured input, to individual ones of at least the additional set of items, including at least some unstructured input which does not include names of referenced items of the additional set of items;
• and
• deploying the trained iteration-specific version of the one or more neural network models to process unstructured input directed to the particular application.