  Best practices for high conflict conversations like counseling or customer
support almost always include recommendations to paraphrase the previous
speaker. Although paraphrase classification has received widespread attention
in NLP, paraphrases are usually considered independent from context, and common
models and datasets are not applicable to dialog settings. In this work, we
investigate paraphrases in dialog (e.g., Speaker 1: "That book is mine."
becomes Speaker 2: "That book is yours."). We provide an operationalization of
context-dependent paraphrases, and develop a training for crowd-workers to
classify paraphrases in dialog. We introduce a dataset with utterance pairs
from NPR and CNN news interviews annotated for context-dependent paraphrases.
To enable analyses on label variation, the dataset contains 5,581 annotations
on 600 utterance pairs. We present promising results with in-context learning
and with token classification models for automatic paraphrase detection in
dialog.
