论文标题
ATP：Amrize然后解析！增强AMR解析使用假人
ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
论文作者
论文摘要
由于抽象含义表示（AMR）隐含地涉及复合语义注释，因此我们假设在语义上或正式相关的辅助任务可以更好地增强AMR解析。我们发现1）语义角色标签（SRL）和依赖性解析（DP）将带来比其他任务（例如文本到AMR过渡中的MT和摘要，即使数据更少。 2）为了使AMR更适合AMR，辅助任务的数据应在培训前正确地“ AMRRAMR”到假AMR。从浅层解析任务中的知识可以更好地转移到AMR解析，并通过结构转换。 3）与多任务学习相比，中级学习是为AMR解析引入辅助任务的更好范式。从经验的角度来看，我们提出了一种有原则的方法来涉及辅助任务以提高AMR解析。广泛的实验表明，我们的方法在不同的基准测试方面取得了新的最新性能，尤其是在与拓扑相关的分数中。
As Abstract Meaning Representation (AMR) implicitly involves compound semantic annotations, we hypothesize auxiliary tasks which are semantically or formally related can better enhance AMR parsing. We find that 1) Semantic role labeling (SRL) and dependency parsing (DP), would bring more performance gain than other tasks e.g. MT and summarization in the text-to-AMR transition even with much less data. 2) To make a better fit for AMR, data from auxiliary tasks should be properly "AMRized" to PseudoAMR before training. Knowledge from shallow level parsing tasks can be better transferred to AMR Parsing with structure transform. 3) Intermediate-task learning is a better paradigm to introduce auxiliary tasks to AMR parsing, compared to multitask learning. From an empirical perspective, we propose a principled method to involve auxiliary tasks to boost AMR parsing. Extensive experiments show that our method achieves new state-of-the-art performance on different benchmarks especially in topology-related scores.
