Abstract
We propose a new domain adaptation method
for Combinatory Categorial Grammar (CCG)
parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our solution is
conceptually simple, and not relying on a specific parser architecture, making it applicable
to the current best-performing parsers. We
conduct extensive parsing experiments with
detailed discussion; on top of existing benchmark datasets on (1) biomedical texts and
(2) question sentences, we create experimental datasets of (3) speech conversation and (4)
math problems. When applied to the proposed
method, an off-the-shelf CCG parser shows
significant performance gains, improving from
90.7% to 96.6% on speech conversation, and
from 88.5% to 96.8% on math problems