As people all over the world adopt machine translation (MT) to communicate across languages, there is increased need for affordances that aid users in understanding when to rely on automated translations. Identifying the information and interactions that will most help users meet their translation needs is an open area of research at the intersection of Human-Computer Interaction (HCI) and Natural Language Processing (NLP). This paper advances work in this area by drawing on a survey of users' strategies in assessing translations. We identify three directions for the design of translation systems that support more reliable and effective use of machine translation: helping users craft good inputs, helping users understand translations, and expanding interactivity and adaptivity. We describe how these can be introduced in current MT systems and highlight open questions for HCI and NLP research.