Treehouse Talk

A Rebel Alliance in Babel's Aftermath: Combining rules and probabilities in machine translation

Dan Flickinger

CSLI, Stanford University

During the past 10 to 15 years, machine translation has experienced renewed and growing interest, driven in part by new applications and markets on the Web, and in part by the invention of new approaches, in particular data-driven methods like Statistical Machine Translation (SMT) and Example-Based MT. While these methods have shown very promising initial results, it has recently become clear even to proponents of SMT that further improvements in quality of output will require something in addition to the current statistical methods alone. There is an emerging consensus within computational linguistics that hybrid approaches combining rich symbolic resources and powerful statistical techniques will be necessary to produce NLP applications with a satisfactory balance of robustness and precision. In this talk, I will present and demonstrate one such hybrid approach in a semantic-transfer based MT system, LOGON, developed in Norway, which makes use of two wide-coverage hand-built grammars of Norwegian (an LFG grammar) and English (an HPSG grammar) to parse and generate, combined with statistical methods to rank the outputs of each of the components for analysis, transfer, and generation.

(Talk presented 2/20/08)

-- Main.ebender - 18 Feb 2008