1 research outputs found
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation
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.Comment: 11 pages, accepted as long paper to ACL 2019 Ital