  In this paper we present an application of explanation-based learning (EBL)
in the parsing module of a real-time English-Spanish machine translation system
designed to translate closed captions. We discuss the efficiency/coverage
trade-offs available in EBL and introduce the techniques we use to increase
coverage while maintaining a high level of space and time efficiency. Our
performance results indicate that this approach is effective.
