论文标题
通过指向有效的选区解析
Efficient Constituency Parsing by Pointing
论文作者
论文摘要
我们提出了一个新颖的选区解析模型，该模型将解析问题投入到一系列指向任务中。具体而言，我们的模型估计跨度是通过对应于跨度边界单词的指向分数合法树成分的可能性。我们的解析模型支持有效的自上而下解码，我们的学习目标能够在不采取昂贵的CKY推理的情况下执行结构一致性。标准英语Penn Treebank解析任务上的实验表明，我们的方法在不使用预训练模型的情况下达到了92.78 F1，该模型比所有具有相似时间复杂性的现有方法高。使用预训练的BERT，我们的模型达到了95.48 F1，这在更快的同时与最先进的竞争力。我们的方法还建立了在SPMRL多语言选区解析的SPMRL共享任务中的巴斯克地区和瑞典人的最新最新。
We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate tree constituent via the pointing score corresponding to the boundary words of the span. Our parsing model supports efficient top-down decoding and our learning objective is able to enforce structural consistency without resorting to the expensive CKY inference. The experiments on the standard English Penn Treebank parsing task show that our method achieves 92.78 F1 without using pre-trained models, which is higher than all the existing methods with similar time complexity. Using pre-trained BERT, our model achieves 95.48 F1, which is competitive with the state-of-the-art while being faster. Our approach also establishes new state-of-the-art in Basque and Swedish in the SPMRL shared tasks on multilingual constituency parsing.
