String-to-Dependency Statistical Machine Translation

Citations Over TimeTop 10% of 2010 papers

Abstract

We propose a novel string-to-dependency algorithm for statistical machine translation. This algorithm employs a target dependency language model during decoding to exploit long distance word relations, which cannot be modeled with a traditional n-gram language model. Experiments show that the algorithm achieves significant improvement in MT performance over a state-of-the-art hierarchical string-to-string system on NIST MT06 and MT08 newswire evaluation sets.

Related Papers

• → Primary atomic frequency standards at NIST(2001)31 cited
• Replicating UTC(NIST) at Remote Sites(2012)
• → Assessing the Impact of the National Institute of Standards and Technology’s Research Collaborations(2014)1 cited
• → Evaluation of National Institute of Standards and Technology (NIST) Thermal Conductivity Standard Reference Materials Using a 20-cm Heat Flow Meter Apparatus(1989)1 cited
• → Technique for assessing PNA measurement repeatability using a NIST standard(2005)1 cited