Learning latent variable grammars from complementary perspectives

The corpus for training a parser consists of sentences of heterogeneous grammar usages. Previous parser domain adaptation work has concentrated on adaptation to the shifts in vocabulary rather than grammar usage. In this paper, we focus on exploiting the diversity of training date separately and then accumulates their advantages. We propose an approach that grammar is biased toward relevant syntactic style, and the complementary grammar usage are combined for inference. Multiple grammars with partly complementary points of strength are induced individually. They capture complementary data representation, and we accumulates their advantages in a joint model to assemble the complementary depicting powers. Despite its compatibility with many other methods, out product model achieves 85.20% F₁ score on Penn Chinese Treebank, higher than previous systems.