University of Cambridge > Talks.cam > NLIP Seminar Series > A Rebel Alliance in Babel's Aftermath: Combining rules and probabilities in machine translation

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

Add to your list(s) Download to your calendar using vCal

  • UserDan Flickinger, CSLI Stanford University and Cambridge Computer Laboratory
  • ClockFriday 23 November 2007, 12:00-13:00
  • HouseSW01 Computer Laboratory.

If you have a question about this talk, please contact Johanna Geiss.

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

Date: Friday 23/11/2007, 12.00pm. Room: SW01

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 and English to parse and generate, combined with statistical methods to rank the outputs of each of the components for analysis, transfer, and generation.

This talk is part of the NLIP Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2022 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity