Abstract
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.
Abstract (translated by Google)
在本文中，我们提出了解释学习（EBL）在实时英语 - 西班牙语机器翻译系统的解析模块中的应用，用于翻译隐藏式字幕。我们讨论EBL中可用的效率/覆盖率折衷，并介绍我们用来增加覆盖率的技术，同时保持高水平的空间和时间效率。我们的表现结果表明这种方法是有效的。
URL
https://arxiv.org/abs/cs/9907008