论文标题
通过自学来推动AMR解析的极限
Pushing the Limits of AMR Parsing with Self-Learning
论文作者
论文摘要
由于转移学习的影响和AMR特定的新型体系结构的发展，在过去两年中，抽象意义表示（AMR）解析在过去两年中的性能显着增长。同时，自学习技术有助于推动其他自然语言处理应用程序的性能界限，例如机器翻译或问答。在本文中，我们探讨了可以应用训练有素的模型来提高AMR解析性能的不同方式，包括生成合成文本和AMR注释以及对Oracle的改进。我们表明，在没有任何其他人类注释的情况下，这些技术可以改善已经表现的解析器，并在AMR 1.0和AMR 2.0上实现最新结果。
Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR. At the same time, self-learning techniques have helped push the performance boundaries of other natural language processing applications, such as machine translation or question answering. In this paper, we explore different ways in which trained models can be applied to improve AMR parsing performance, including generation of synthetic text and AMR annotations as well as refinement of actions oracle. We show that, without any additional human annotations, these techniques improve an already performant parser and achieve state-of-the-art results on AMR 1.0 and AMR 2.0.
