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Improving AMR parsing by exploiting the dependency parsing as an auxiliary task

机译：通过利用解析作为辅助任务来改善AMR解析

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摘要

meaning representations (AMRs) represent sentence semantics as rooted labeled directed acyclic graphs. Though there is a strong correlation between the AMR graph of a sentence and its corresponding dependency tree, the recent neural network AMR parsers do neglect the exploitation of dependency structure information. In this paper, we explore a novel approach to exploiting dependency structures for AMR parsing. Unlike traditional pipeline models, we treat dependency parsing as an auxiliary task for AMR parsing under the multi-task learning framework by sharing neural network parameters and selectively extracting syntactic representation by the attention mechanism. Particularly, to balance the gradients and focus on the AMR parsing task, we present a new dynamical weighting scheme in the loss function. The experimental results on the LDC2015E86 and LDC2017T10 dataset show that our dependency-auxiliary AMR parser significantly outperforms the baseline and its pipeline counterpart, and demonstrate that the neural AMR parsers can be greatly boosted with the help of effective methods of integrating syntax.
机译：意义表征（AMRS）代表遗传语义，如植物标记为标记的有向非循环图形。虽然句子的AMR图与其相应的依赖树之间存在强烈的相关性，但最近的神经网络AMR解析器忽略了依赖结构信息的开发。在本文中，我们探索了利用AMR解析的依赖性结构的新方法。与传统管道模型不同，我们通过共享神经网络参数并选择性地提取注意机制选择性地提取句法表示，将依赖性解析为AMR解析的辅助任务。特别是，为了平衡梯度并专注于AMR解析任务，我们在丢失功能中提出了一种新的动态加权方案。 LDC2015E86和LDC2017T10数据集上的实验结果表明，我们的依赖 - 辅助AMR解析器显着优于基线及其管道对应物，并证明了神经AMR解析器可以在整合语法的有效方法的帮助下大大提升。

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