The deployment of learning and matching techniques in the area of machine translation, first advocated in the early 80s (Nagao 84) proposed as" Translation by Analogy" and the return of statistical methods in the early 90's (Brown et al. 93) have given rise to much discussion as to the architecture and constituency of modern machine translation systems. Bilingual text processing and in particular text alignment with the resulting exploitation of information extracted from thus derived examples has turned into a new wave in machine translation (MT).Traditional Rule-Based Machine Translation (RBMT) systems suffer from tractability, adaptability as well as quality and performance problems. Example-based Machine Translation (EBMT) also known as Memory-based Machine Translation (MBMT) has attempted to provide alternative ways to overcome the knowledge acquisition bottleneck, yielding promising results.