arXiv:2212.10465 · 中英对照阅读

Soda: Million-scale Dialogue Distillation with Social Commonsense Contextualization

Hyunwoo Kim、Jack Hessel、Liwei Jiang、Peter West、Ximing Lu、Youngjae Yu、Pei Zhou、Ronan Le BrasMalihe Alikhani†、Gunhee Kim、Maarten Sap、Yejin Choi♡\heartsuit Allen Institute for Artificial Intelligence♠\spadesuit Seoul National University♢\diamondsuit University of Washington♣\clubsuit University of Southern California†\dagger University of Pittsburgh‡\ddagger Carnegie Mellon University

摘要

Data scarcity has been a long standing issue in the field of open-domain social dialogue. To quench this thirst, we present Soda: the first publicly available, million-scale high-quality social dialogue dataset. By contextualizing social commonsense knowledge from a knowledge graph, we are able to distill an exceptionally broad spectrum of social interactions from a large language model. Human evaluation shows that conversations in Soda are more consistent, specific, and (surprisingly) natural than those in prior human-authored datasets.

原文 arXiv:2212.10465