Fast and Accurate Span-based Semantic Role Labeling as Graph Parsing

Anonymous

Abstract: Currently, BIO-based and Tuple-based approaches perform quite well on the span-based semantic role labeling (SRL) task. However, the BIO-based approach usually needs to encode a sentence once for each predicate when predicting its arguments, and the Tuple-based approach has to deal with a huge search space of $O(n^3)$, greatly reducing the training and inference efficiency. Moreover, both BIO-based and Tuple-based approaches usually consider only local structural information when making predictions.This paper proposes to cast end-to-end span-based SRL as a graph parsing task. Based on a novel graph representation schema, we present a fast and accurate SRL parser on the shoulder of recent works on high-order semantic dependency graph parsing (SDGP). Moreover, we propose a constrained Viterbi procedure to ensure the legality of the output graph. Experiments on CoNLL05, CoNLL12, and Chinese Proposition Bank 1.0 (CPB1.0) datasets show that our model achieves new state-of-the-art results and can parse over 600 sentences per second.

Paper Type: long