Statistical Machine Translation (SMT) accuracy degrades when there is only a limited amount of training, or when the training is not from the same domain or genre of text as the target application. However, cross-domain applications are typical of many real world tasks. We demonstrate that SMT accuracy can be improved in a cross-domain application by using a controlled language (CL) interface to help reduce lexical ambiguity in the input text. Our system, CL-MT, presents a monolingual user with a choice of word senses for each content word in the input text. CL-MT temporarily adjusts the underlying SMT system's phrase table, boosting the scores of translations that include the word senses preferred by the user and lowering scores for disfavored translations. We demonstrate that this improves translation adequacy in 33.8% of the sentences in Spanish to English translation of news stories, where the SMT system was trained on proceedings of the European Parliament.