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 ⊆

Style is an integral part of natural language.However, evaluation methods for style measures are rare, often task-specific and usually do not control for content.We propose the modular, fine-grained and contentcontrolled similarity-based STyle EvaLuation framework (STEL) to test the performance of any model that can compare two sentences on style.We illustrate STEL with two general dimensions of style (formal/informal and simple/complex) as well as two specific characteristics of style (contrac'tion and numb3r substitution).We find that BERT-based methods outperform simple versions of commonly used style measures like 3-grams, punctuation frequency and LIWC-based approaches.We invite the addition of further tasks and task instances to STEL and hope to facilitate the improvement of style-sensitive measures.