  Semantic role labeling (SRL) aims at elaborating the meaning of a sentence by
forming a predicate-argument structure. Recent researches depicted that the
effective use of syntax can improve SRL performance. However, syntax is a
complicated linguistic clue and is hard to be effectively applied in a
downstream task like SRL. This work effectively encodes syntax using adaptive
convolution which endows strong flexibility to existing convolutional networks.
The existing CNNs may help in encoding a complicated structure like syntax for
SRL, but it still has shortcomings. Contrary to traditional convolutional
networks that use same filters for different inputs, adaptive convolution uses
adaptively generated filters conditioned on syntactically informed inputs. We
achieve this with the integration of a filter generation network which
generates the input specific filters. This helps the model to focus on
important syntactic features present inside the input, thus enlarging the gap
between syntax-aware and syntax-agnostic SRL systems. We further study a
hashing technique to compress the size of the filter generation network for SRL
in terms of trainable parameters. Experiments on CoNLL-2009 dataset confirm
that the proposed model substantially outperforms most previous SRL systems for
both English and Chinese languages
