MT and many other NLP systems have made steady and impressive progress while use of explicit semantic processing has undergone a rise and fall. It is also true that consensus among researchers on the meaning of meaning has remained elusive. In this chapter, however, the author observes renewed interest in semantic representation and processing. Moreover, he foresees gradual adoption of semantic approaches grounded upon audio, visual, or other sensor-based input. He distinguishes such perceptually-grounded semantic approaches from most current methods. The author surveys the role of semantics in machine translation to date in terms of three paradigms: rule-based, statistical, and neural MT. A section on each paradigm discusses its treatment of semantics: rule-based methods have generally emphasized symbolic semantics; statistical methods have generally avoided semantic treatment or employed vector-based semantics; and neural methods have handled meaning as implicit within networks.