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.
翻译:目前,基于BIO和基于Tuple的方法在基于跨边界的语义作用标签(SRL)任务上表现得相当不错,然而,基于BIO的办法通常需要在预测其论点时为每个前提编码一次句子,而在预测其论点时,基于Tuple的办法必须处理一个巨大的搜索空间,即$(n)3美元,大大降低了培训和推论效率。分析速度低于每秒50个句子。此外,基于BIO和基于Tuple的办法通常在作出预测时只考虑当地的结构信息。本文提议将基于终端到终端的基于跨边界的SRL作为图表解析任务。基于新颖的图表表达方式,我们在近期关于高顺序的语义依赖图解析的作品的肩膀上提出了一个快速和准确的SRL分析器。此外,我们提出了一种限制性的维特比程序,以确保产出图的合法性。关于600 Engin CoNLL05和CONL12数据集的实验表明,我们的模型可以在两种情况下实现新的状态至终端的二次判决结果,在两种情况下可以不经经过训练的语前的模型。