Adaptive Convolution for Semantic Role Labeling

TL;DR

This paper introduces an adaptive convolution approach that dynamically encodes syntactic information to significantly improve semantic role labeling performance across multiple languages.

Contribution

It proposes a novel adaptive convolution method with a filter generation network to better incorporate syntax into SRL models, enhancing accuracy.

Findings

Outperforms previous SRL systems on CoNLL-2009 dataset

Effective in both English and Chinese languages

Uses a hashing technique to reduce model size

Abstract

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