Abstract: As robots increasingly collaborate with humans, natural language provides an intuitive interface for communication about physical actions. However, bridging the gap between linguistic descriptions and physical forces remains challenging for enabling robots to interpret movement instructions and communicate their intended actions. We address learning a shared embedding space between time-series force data and natural language motion descriptions for human-robot interaction. Our framework maps both force curves and phrases into a common latent space using data augmentation, feature engineering, and multitask learning to enable bidirectional translation. Evaluation with 10 participants performing motions with a robot arm demonstrates our model learns meaningful embeddings that effectively translate between forces and language descriptions. This will help robots learn appropriate verbal communication patterns while physically interacting with humans during collaborative tasks.