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

by   Shilin Zhou, et al.

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. The parsing speed is less than 50 sentences per second. 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. Moreover, we propose a constrained Viterbi procedure to ensure the legality of the output graph. Experiments on English CoNLL05 and CoNLL12 datasets show that our model achieves new state-of-the-art results under both settings of without and with pre-trained language models, and can parse over 600 sentences per second.



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