What's Mine becomes Yours: Defining, Annotating and Detecting Context-Dependent Paraphrases in News Interview Dialogs

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 across turns 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 ContextDeP, a dataset with utterance pairs from NPR and CNN news interviews annotated for contextdependent paraphrases. To enable analysis 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. What? Shortened Examples Clear Contextual Equivalence ⊆ CP Guest: I know they are cruel. Host: You know they are cruel. G: We have been the punching bag of the president. H: The president has been using Chicago as a punching bag. 1 https://github.com/nlpsoc/ Paraphrases-in-News-Interviews 2 https://huggingface.co/datasets/AnnaWegmann/ Paraphrases-in-Interviews 3 This is in line with the license from the original data publication (Zhu et al., 2021) .