Before revision:  As the label set generalizes that of previous tasks ( DiMSUM, PARSEME ), we additionally evaluate how well the model generalizes to those test sets, with encouraging results. By establishing baseline models and evaluation metrics , we pave the way for comprehensive and accurate modeling of lexical semantics  .
After revision:  As the label set generalizes that of previous tasks ( DiMSUM, PARSEME ), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics , we pave the way for comprehensive and accurate modeling of lexical semantics .