Towards a Unified Approach to Memory- and Statistical-Based Machine Translation

We present a set of algorithms that enable us to translate natural language sentences by exploiting both a translation memory and a statistical-based translation model. Our results show that an automatically derived translation memory can be used within a statistical framework to often find translations of higher probability than those found using solely a statistical model. The translations produced using both the translation memory and the statistical model are significantly better than translations produced by two commercial systems: our hybrid system translated perfectly 58% of the 505 sentences in a test collection, while the commercial systems translated perfectly only 40-42% of them.

• Type: Conference
• Year: 2001
• Where: ACL
• Authors: Daniel Marcu
• Keywords: ACL 2001, ACL 2007, Statistical Model, Statistical-based Translation Model, Translation Memory