Before revision:  Observers are tokens that are not attended to, and are an alternative to the [CLS] token . The proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWU ) in both the full data and few-shot (10 examples per intent) settings.
After revision:  Observers are tokens that are not attended to, and are an alternative to the [CLS] token as a semantic representation of utterances. Example-driven training learns to classify utterances by comparing to examples, thereby using the underlying encoder as a sentence similarity model. These methods are complementary; improving the representation through observers allows the example-driven model to better measure sentence similarities. When combined, the proposed methods attain state-of-the-art results on three intent prediction datasets ( Banking, Clinc , and HWU ) in both the full data and few-shot (10 examples per intent) settings.