论文标题
使用变压器的开放域框架语义解析
Open-Domain Frame Semantic Parsing Using Transformers
论文作者
论文摘要
框架语义解析是一个复杂的问题，其中包括多个基础子任务。最近的方法采用了子任务的联合学习（例如谓词和参数检测），以及对相关任务的多任务学习（例如句法和语义解析）。在本文中，我们通过基于变压器的模型探索所有子任务的多任务学习。我们表明，纯粹的生成式编码器架构可以轻松地击败Framenet 1.7解析的先前艺术状态，而混合解码的多任务方法可以实现更好的性能。最后，我们表明，多任务模型还胜过最新的在Conll 2012基准上解析的最新系统系统。
Frame semantic parsing is a complex problem which includes multiple underlying subtasks. Recent approaches have employed joint learning of subtasks (such as predicate and argument detection), and multi-task learning of related tasks (such as syntactic and semantic parsing). In this paper, we explore multi-task learning of all subtasks with transformer-based models. We show that a purely generative encoder-decoder architecture handily beats the previous state of the art in FrameNet 1.7 parsing, and that a mixed decoding multi-task approach achieves even better performance. Finally, we show that the multi-task model also outperforms recent state of the art systems for PropBank SRL parsing on the CoNLL 2012 benchmark.
