Metapaths with specific complex semantics are critical to learning diverse semantic and structural information of heterogeneous networks (HNs) for most of the existing representation learning models. However, any metapaths consisting of multiple, simple metarelations must be driven by domain experts. These sensitive, expensive, and limited metapaths severely reduce the flexibility and scalability of the existing models. A metapath-free, scalable representation learning model, called Metarelation2vec, is proposed for HNs with biased joint learning of all metarelations in a bid to address this problem. Specifically, a metarelation-aware, biased walk strategy is first designed to obtain better training samples by using autogenerating cooperation probabilities for all metarelations rather than using expert-given metapaths. Thereafter, grouped nodes by the type, a common and shallow skip-gram model is used to separately learn structural proximity for each node type. Next, grouped links by the type, a novel and shallow model is used to separately learn the semantic proximity for each link type. Finally, supervised by the cooperation probabilities of all meta-words, the biased training samples are thrown into the shallow models to jointly learn the structural and semantic information in the HNs, ensuring the accuracy and scalability of the models. Extensive experimental results on three tasks and four open datasets demonstrate the advantages of our proposed model.