计算机科学
自然语言处理
域适应
人工智能
判决
管道(软件)
图形
程序设计语言
理论计算机科学
分类器(UML)
作者
Ming Zhao,Yaling Wang,Yves Lepage
标识
DOI:10.1109/icacsis56558.2022.9923502
摘要
Abstract Meaning Representation (AMR) is a broad -coverage formalism for capturing the semantics of a given sentence. However, domain adaptation of AMR is limited by the shortage of annotated AMR graphs. In this paper, we explore and build a new large-scale dataset with 2.3 million AMRs in the domain of academic writing. Additionally, we prove that 30% of them are of similar quality as the annotated data in the downstream AMR-to-text task. Our results outperform previous graph-based approaches by over 11 BLEU points. We provide a pipeline that integrates automated generation and evaluation. This can help explore other AMR benchmarks.
科研通智能强力驱动
Strongly Powered by AbleSci AI