Before revision:  We propose two approaches for improving the generalizability of utterance classification models: (1) example-driven training and (2) observers. Example-driven traininglearns to classify utterances by comparing to examples, thereby using the underlying encoder as a sentence similarity model . Prior work has shown that BERT-like models tend to attribute a significant amount of attention to the [CLS] token, which we hypothesize results in diluted representations.
After revision:  We propose two approaches for improving the generalizability of utterance classification models: (1) example-driven training and (2) example-driven training . Prior work has shown that BERT-like models tend to attribute a significant amount of attention to the [CLS] token, which we hypothesize results in diluted representations.