Not quite. This is a technique for generating new training data by inserting API calls in existing sentences, and filtering out API calls that don't help the network predict the complete sentence.
An example from the paper:
The training data includes the sentence: "Pittsburgh is also known as the Steel City."
They generate candidates including:
Pittsburgh is also known as [QA(What other name is Pittsburgh known by? → Steel City)]
the Steel City.
Pittsburgh is also known as [QA(Which country is Pittsburgh in? → United States)]
the Steel City.
Then they add the first sentence to the training data because its response is useful, and ignore the second because its response is not useful.
That allows them to generate enough training data with API calls to train a network that uses API calls when responding to future requests.
An example from the paper:
The training data includes the sentence: "Pittsburgh is also known as the Steel City."
They generate candidates including:
Pittsburgh is also known as [QA(What other name is Pittsburgh known by? → Steel City)] the Steel City.
Pittsburgh is also known as [QA(Which country is Pittsburgh in? → United States)] the Steel City.
Then they add the first sentence to the training data because its response is useful, and ignore the second because its response is not useful.
That allows them to generate enough training data with API calls to train a network that uses API calls when responding to future requests.