Development of structured semantic models to improve the quality of statistical machine translation systems

• Applicant: Dr. Hagen Fürstenau
• Subject Area: Image and Language Processing, Computer Graphics and Visualisation, Human Computer Interaction, Ubiquitous and Wearable Computing
• Term: Funded in 2012
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 200135679

Modern machine translation systems are based on statistical methods that try to find the most probable translation of a given input sentence. Typically, words and strings of words are translated without representing their meaning on an abstract level. This can lead to fluent output sentences, which nonetheless fail to adequately render the meaning of the input sentence. The proposed project is concerned with the development of models that can rank possible translations by their semantic compatibility with the original sentence and thus find better translations. The meanings of input and output sentences are represented in probabilistic models featuring situations, semantic roles and concepts. In contrast to previous approaches in role semantics, no general concepts are to be specified in advance. Instead, appropriate categories for classification will be learned from available text corpora. This would allow statistical machine translation systems to directly take advantage of structured semantic information for the first time. While the main goal of the project is to improve the quality of machine translation systems, the new semantic models will also make it possible to study task-oriented semantic categories and their generalizability across languages, which may also benefit other areas in computational linguistics.

• DFG Programme: Research Fellowships
• International Connection: USA
• Host: Professor Dr. Eduard Hovy