Unification-Based Glossing

We present an approach to syntax-based machine translation that combines unification-style interpretation with statistical processing. This approach enables us to translate any Japanese newspaper article into English, with quality far better than a word-for-word translation. Novel ideas include the use of feature structures to encode word lattices and the use of unification to compose and manipulate lattices. Unification also allows us to specify abstract features that delay target-language synthesis until enough source-language information is assembled. Our statistical component enables us to search efficiently among competing translations and locate those with high English fluency. 1 Background JAPANGLOSS [ Knight et al., 1994; 1995 ] is a project whose goals are to scale up knowledge-based machine translation (KBMT) techniques to handle JapaneseEnglish newspaper MT, to achieve higher quality output than is currently available, and to develop techniques for rapidly constructing MT ..