Evaluation Techniques Applied to Domain Tuning of MT
Lexicons
We describe a set
of evaluation techniques applied to domain tuning of bilingual lexicons for
machine translation. Our overall
objective is to translate a domain-specific document in a foreign language (in
this case, Chinese) to English. First,
we perform an intrinsic evaluation of the effectiveness of our domain-tuning
techniques by comparing our domain-tuned lexicon to a manually constructed
domain-specific bilingual termlist. Our results indicate that we achieve 66%
recall and 95% precision with respect to a human-derived gold standard. Next,
an extrinsic evaluation demonstrates that our domain-tuned lexicon improves the
Bleu scores 50% over a statistical system -with a smaller improvement when the
system is trained on a uniformly-weighted dictionary.
For the colloquium series schedule, see the UMD Computational
Linguistics Colloquium Series web page at
http://umiacs.umd.edu/~resnik/cl_colloquium/. If you are interested in meeting
with the speaker, please contact Doug
Oard (oard@umiacs.umd.edu).