资源分类
》 资源 》论文》AMR Parsing as Sequence-to-Graph Transduction

AMR Parsing as Sequence-to-Graph Transduction

2019-09-18 | |  80 |   67 |   0 0 0
Abstract We propose an attention-based model that treats AMR parsing as sequence-to-graph transduction. Unlike most AMR parsers that rely on pre-trained aligners, external semantic resources, or data augmentation, our proposed parser is aligner-free, and it can be effectively trained with limited amounts of labeled AMR data. Our experimental results outperform all previously reported SMATCH scores, on both AMR 2.0 (76.3% F1 on LDC2017T10) and AMR 1.0 (70.2% F1 on LDC2014T12).

上一篇：A Wind of Change:Detecting and Evaluating Lexical Semantic Changeacross Times and Domains

下一篇：An Empirical Study of Span Representationsin Argumentation Structure Parsing

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...