Logical and Statistical Approaches to Mismatch Resolution
Co-Principal Investigators 
Project Summary A major bottleneck in present-day machine translation (MT) is identifying appropriate approximations when no exact translation exists between the source and target languages. An MT system must resolve mismatches either by incorporating implicit information from context or leaving out some information in the source text.
We propose to implement a prototype MT system incorporating the usual MT steps, Analysis (analyzing Japanese sources into a Japanese-oriented semantic representation), Transfer (transferring the Japanese-oriented semantics into an English-oriented semantics), and Generation (building English targets from the transferred semantics), and adding a novel Mismatch Resolution Module (MRM) called when Generation fails.
In this architecture, both Analysis and Generation modules will be purely monolingual, and Transfer is simplistic, incorporating minimal context information, and the bulk of mismatch resolution and disambiguation is done in the Generation-MRM loop, where the MRM offers solutions to the problems encountered by Generation. We explore two kinds of MRMs, logical and statistical, with the design goal of a single MRM combining the advantages of both approaches.
Translations are evaluated by monolingual English speakers applying evaluation measures modeled on those of the DARPA MT program. The focus will be on translating the Japanese joint venture business articles in the MUC-5 corpus into English.
The proposed MT project attacks the crucial mismatch resolution problem with a novel architecture, logical and statistical techniques, and on-line text resources.
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